I HC QUC GIA H NI TRNG I HC CNG NGH

inh Xun Nht

NGHIN CU CC THUT TON NHN DNG CM XC KHUN MT TRN NH 2D

KHO LUN TT NGHIP I HC H CHNH QUY

Ngnh: Cng ngh thng tin

H NI 2010

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I HC QUC GIA H NI TRNG I HC CNG NGH

inh Xun Nht

NGHIN CU CC THUT TON NHN DNG CM XC KHUN MT TRN NH 2D

KHO LUN TT NGHIP I HC H CHNH QUY

Ngnh: Cng ngh thng tin Cn b hng dn: PGS TS. Bi Th Duy

H NI 2010

LI CM NLi u tin em xin by t lng bit n ti cc thy, c gio trong trng i hc Cng ngh - i hc Quc gia H Ni. Cc thy c dy bo, ch dn chng em v lun to iu kin tt nht cho chng em hc tp trong sut qu trnh hc i hc c bit l trong thi gian lm kho lun tt nghip. Em xin by t lng bit n su sc ti PGS TS. Bi Th Duy, thy hng dn em tn tnh trong hc k va qua. Ti cng xin cm n nhng ngi bn ca mnh, cc bn lun bn ti, gip v cho ti nhng kin ng gp qu bu trong hc tp cng nh trong cuc sng. Cui cng con xin gi ti b m v ton th gia nh lng bit n v tnh cm yu thng nht. Con xin dnh tng b m kt qu m con t c trong sut bn nm hc i hc. Con cm n b m nhiu. H ni, ngy 25/05/2010

inh Xun Nht

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TM TTBi ton nhn dng cm xc bt u c nghin cu t nhng nm 1970 nhng kt qu t c vn cn nhiu hn ch. Hin nay vn ny vn ang c rt nhiu ngi quan tm bi tnh hp dn cng nhng vn phc tp ca n. Mc tiu ca kha lun ny l nghin cu v nh gi v cc phng php nhn dng mt ngi trong vic nhn dng ra 5 cm xc c bn: Vui, bun, gh tm, dn gi v t nhin trn nh tnh, chnh din. T kha: Facial Expression Recognition, Principal Component Analysis, Neural Network, Decision Tree, Weka

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MC LCLI CM N...................................................................................................................2 TM TT.........................................................................................................................3 DANH MC HNH NH................................................................................................6 GII THIU.....................................................................................................................7 Cu trc ca kha lun..........................................................................................7 Nhn dng cm xc khun mt v ng dng........................................................7 Mt s phng php nhn dng cm xc khun mt............................................8 Cc phng php da trn c trng ca nh...............................................8 Phng php s dng Action Units..............................................................9 Phng php dng m hnh AAM kt hp tng quan im.......................9 M hnh tng quan......................................................................................10 Cc thch thc trong vn nhn dng cm xc khun mt..............................11 Cc vn lin quan............................................................................................11 MT S L THUYT C BN..................................................................................13 Gii thiu v mng nron[6]...............................................................................13 Mng Perceptron nhiu tng (MPL Multi Perceptron Layer)..................14 nh x mng lan truyn tin.......................................................................14 Hm sigmoid...............................................................................................17 Thut ton lan truyn ngc........................................................................18 Gii thiu v PCA...............................................................................................25 Mt s khi nim ton hc..........................................................................25 Ma trn i s..............................................................................................28 Eigenvector (Vect ring)...........................................................................29 Eigenvalue (Gi tr ring)............................................................................29 0.1.1 Phn tch thnh phn chnh (PCA)......................................................29 Chng 1. CC PHNG PHP NHN DNG CM XC KHUN MT............30

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Nhn dng cm xc da trn PCA truyn thng.................................................31 Trch chn c trng...................................................................................31 Qu trnh nhn dng....................................................................................32 Nhn dng cm xc da trn PCA kt hp cc thut ton hc...........................32 Mng nron.................................................................................................32 Cy quyt nh............................................................................................33 Chng 2. THC NGHIM...........................................................................................34 Mi trng thc nghim......................................................................................34 D liu u vo...................................................................................................35 Kho st v nh gi............................................................................................35 Phng php PCA truyn thng..................................................................35 Phng php s dng mng nron..............................................................36 Phng php s dng cy quyt nh.........................................................36 2.1 Tng kt ........................................................................................................37 Chng 3. KT LUN...................................................................................................38 PH LC - MT S THUT NG ANH VIT......................................................39 TI LIU THAM KHO..............................................................................................40

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DANH MC HNH NHHnh 1: M hnh nhn dng cm xc...............................................................................10 Hnh 2: M hnh mng lan truyn tin.............................................................................14 Hnh 3: th hm truyn sigmoid..........................................................................17 Hnh 4: Lan truyn ngc................................................................................................20 Hnh 5: Minh ha vic tnh j cho vic tnh nt n j.......................................................23 Hnh 6: V d v 1 non-eigenvector v 1 eigenvector...........................................28 Hnh 7: V d v 1 eigenvector c t l khc vn 1 l eigenvector................................28 Hnh 8: V d v trch chn c trng bng PCA............................................................31 Hnh 9: M hnh mng nron..........................................................................................33 Hnh 10: Cy quyt nh..................................................................................................34

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GII THIUCu trc ca kha lunVi ni dung trnh by nhng l thuyt c bn v cch p dng vo bi ton nhn dng cm xc khun mt, kha lun c t chc theo cu trc nh sau: Chng 1: Gii thiu Gii thiu s lc v cc phng php nhn dng cm xc, ng dng ca n trong cuc sng hng ngy, gii thiu cc phng php c s dng trong kha lun ny, mc tiu v cu trc ca kha lun. Chng 2: Mt s l thuyt c bn Chng hai i vo gii thiu tng quan v cc l thuyt c bn. Nhng kin thc c bn ny l tin ngi c hiu c cch p dng vo bi ton nhn dng cm xc v lp cc bi ton nhn dng ni chung. Chng 3: Cc phng php nhn dng cm xc Chng ny i vo gii thiu mt s phng php nhn dng cm xc s dng cc l thuyt c bn nu chng hai Chng 4: Thc nghim Chng ny phn tch v u, nhc im v so snh, nh gi gia cc phng php. Chng 5: Kt lun Chng ny tng kt li nhng g t c v cha t c. T nu ln nhng hng nghin cu v pht trin tip theo.

Nhn dng cm xc khun mt v ng dngTrong vi nm gn y, cng vi s pht trin v khoa hc v cng ngh, tng tc ngi my tr thnh mt lnh vc ni bt nhm cung cp cho con ngi kh nng phc v ca my mc. iu ny bt ngun t kh nng my mc c th tng tc c vi con ngi. My mc cn cc k nng trao i thng tin vi con ngi v 1 trong nhng k nng l kh nng hiu c cm xc. Cch tt nht mt ngi biu th

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cm xc l qua khun mt. Bi ton nhn dng cm xc khun mt c bt u nghin cu t nhng nm 1970 nhng kt qu t c n nay vn cn nhiu hn ch. ng dng ca nhn dng cm xc trong cuc sng hng ngy l rt ln, cc h thng pht hin trng thi bun ng da vo cm xc trn khun mt c pht trin cnh bo cho ngi li xe khi thy du hiu bun ng, mt mi. Cc h thng kim tra tnh ng n ca thng tin, cc phn mm iu khin da vo cm xc, cc thit b h tr ngi tn tt,... Mc tiu ca kha lun ny l nghin cu 1 s phng php nhn dng cm xc khun mt da trn nh hai chiu v trc din

Mt s phng php nhn dng cm xc khun mtC nhiu phng php c nghin cu gii qut bi ton ny, in hnh l mt s phng php sau: S dng cc n v vn ng trn khun mt (Action units AU), s dng PCA, AAM kt hp tng quan im, s dng cc phng php hc, Mi phng php u c u v nhc im ring. i vi cc phng php s dng PCA kt hp mng nron, cn mt tp d liu chun hun luyn. Vic xy dng cc tp hun luyn ny cng tng i kh khn v tn km v cn nhiu ngi lm mu, nhng ngi ny phi c kh nng din t cm xc tt, ngoi ra cn cn s nh gi ca cc chuyn gia tm l. Hin nay c mt s tp hun luyn chun thng c dng nh JAFFE (Japanese Female Facial Expression) hay Cohn-kanade.

Cc phng php da trn c trng ca nhCc k thut s dng trong phng php ny l phn tch thnh phn chnh PCA, sau hun luyn bng cc thut ton hc. PCA c Karl Pearson to ra nm 1901. n nhng nm 80, Sirovich v Kirby pht trin k thut ny th hin khun mt mt cch hiu qu. a ra s ging nhau gia nhiu hnh nh khun mt khc nhau, k thut ny tm ra nhng thnh phn c bn ca s phn b trn khun mt, th hin bng cc eigenvectors. Tng khun mt trong mt tp hp cc khun mt sau c th tnh xp x bng s kt hp tuyn tnh gia nhng eigenvector ln nht, c bit ti nh eigenfaces.

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Phng php s dng Action UnitsPhng php ny nhn dng cm xc da trn cc n v chuyn ng ca khun mt (AU). C tt c 64 AU, mi AU l s kt hp ca mt s cc c trn khun mt. Cm xc c nhn dng bng cch pht hin ti mt thi im c bao nhiu AU xut hin trn khun mt v vi cc AU xut hin cng nhau tng ng vi 1 cm xc.

Phng php dng m hnh AAM kt hp tng quan imPhng php ny s dng m hnh AAM pht hin khun mt. Sau da vo t l gia 2 mt, lng my, ming, mi, nhn dng cm xc. Kh khn ca

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phng php ny l vic xc nh ngng t l xc nh cm xc. Tuy nhin phng php ny c u im v tc , d thng c ng dng trong nhn dng cm xc thi gian thc.

M hnh tng quannh u vo Pht hin khun mt nh ng vin khun mt

Tin x l nh nh tin x l

Nhn dng cm xc

Cm xc

Hnh 1: M hnh nhn dng cm xc

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Cc thch thc trong vn nhn dng cm xc khun mtXc nh cm xc khun mt l mt bi ton kh bi v con ngi ngoi 7 cm xc c bn, cn rt nhiu cm xc a dng khc. Hn na v nhn dng cm xc da trn cc c im ca khun mt nn thc t khng th bit c cm xc l ng hay khng. V phng php nhn dng, cng gp kh khn khi nh khun mt khng chnh din, qu b, hay trong iu kin nh sng khng tt.

Cc vn lin quanBn cnh vic nhn dng cm xc trong khng gian 2D cn c mt s vn lin quan mt thit. Nhn dng cm xc trong khng gian 3D[10]: y l vn rt gn gi vi nhn dng cm xc trong khng gian 2D, tuy nhin trong khng gian 3D chng ta c nhiu thng tin hn, ngoi mu sc, c trng cn c hnh dng ca khun mt,

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Nhn dng cm xc trong video: Vn ny d dng hn v chng ta c rt nhiu thng tin v khun mt da vo cc khung hnh lin tip, v vn ny cng thc tin hn nhiu so vi nhn dng cm xc trong khng gian 2D.

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MT S L THUYT C BNGii thiu v mng nron[6]C th ni, hin nay, khng c mt nh ngha chnh thc no cho mng neural. Tuy nhin phn ln mi ngi u ng tnh rng mng neural l mt mng bao gm rt nhiu b x l n gin (gi l cc unit), mi unit c vng nh ring ca mnh. Cc unit c kt ni vi nhau thng qua knh thng tin (gi l cc connection), thng mang d liu s (khng phi l cc k hiu), v c m ha theo mt cch no y. Cc unit ch x l trn b d liu ca ring n v trn cc u vo c a ti thng qua cc lin kt. hn ch ca cc php x l cc b ny l n thng trng thi ngh trong sut qu trnh hc. Mt s mng neural l cc m hnh mng neural sinh hc, mt s th khng, nhng t trc ti nay, th tt c cc lnh vc ca mng neural u c nghin cu xy dng xut pht t cc yu cu xy dng cc h thng nhn to rt phc tp, hay cc php x l thng minh, v nhng g tung t nh b no con ngi. Hu ht cc mng neural u c mt vi quy tc hc no m thng qua cc trng s ca cc lin kt c iu chnh da trn d liu. Ni cch khc, cc mng neural hc v cc v d v da trn cc d liu th n c kh nng tng qut tri thc v a ra nhn thc ca mnh. Mng neural l m hnh mng ng dng cc phng php x l song song v cc thnh phn mng x l hon ton c lp vi nhau. Mt vi ngui xem kh nng x l song song s lng ln v tnh lin kt cao ca mng neural l cc tnh cht c trugn ca n. Tuy nhin vi nhng yu cu nh th th li khng c nhng m hnh n gin, v d nh m hnh hi quy tuyn tnh n gin, mt m hnh c ng dng rt rng ri ca mng neural. Mng neural c th c p dng trong mi trng hp khi tn ti mt mi lin h gia cc bin c lp (inputs) v cc bin ph thuc (outputs), thm ch l ngay c khi mi quan h phuc tp. Mt s lnh vc m mng neural c p dng thnh cng nh d on triu chng y hc, d on th trng chng khon, nh gi tin cy ti chnh, iu chnh iu kin ca c cu my mc.

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Mng Perceptron nhiu tng (MPL Multi Perceptron Layer)MPL l mt loi mng lan truyn tin c hun luyn theo kiu hc c gim st. Mng l mt cu trc gm nhiu lp trng s. y ta ch xt n loi mng lan truyn kh vi. y l loi mng c th p dng phng php tnh ton kh hiu qu v mnh gi l lan truyn ngc li , xac inh ao ham ham li theo cac trong s va dc trong mang. y la mt tinh cht rt quan trong cua nhng mang kiu nay bi nhng ao ham nay ong vai tro trung tm trong cac giai thut hoc cua cac mang a lp. Vn lan truyn ngc se c ta xet ti trong mt phn ring sau nay.

nh x mng lan truyn tinTrong phn nay ta se nghin cu m hinh mang neural lan truyn tin nh la mt khung tng quat ai din cho cac ham anh xa phi tuyn gia tp cac bin u vao va tp cac bin u ra. 2.1.2.1 Mng phn lp Cac mang n lp c xy dng da trn s kt hp tuyn tinh cac bin u vao c chuyn i bi mt ham truyn phi tuyn. Ta co th xy dng c cac ham tng quat hn bng cach nghin cu nhng m hinh mang co cac lp cac nut la lin tip, vi cac kt ni t tt ca cac nut thuc mt lp ti tt ca cac nut thuc lp k tip, va khng cho phep bt ky mt loai kt ni nao khac. Nhng mang phn lp nh th nay co th d phn tich hn cac cu truc tng quat khac, va cung d c m phong bi phn mm hn.

Hnh 2: M hnh mng lan truyn tin

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Cac nut khng phai la cac nut nhp va nut xut c goi la cac nut n. Trong m hinh chung ta nghin cu y, co d nut nhp, M nut n va c nut xut. Kt qua cua nut n th j c tinh nh sau: d (1) (1) a j = w ji xi + w j0 i =1 (I.26)

Trong o la trong s cua lp u tin, t nut nhp i n nut n j, va la trong ngng cua nut n j. Gia s t mt bin c inh x0 = 1. T o cng thc (I.26) co th c vit lai: d (1) a j = w ji xi i =0 (I.27)

Sau o hoat ng zk cua nut n j c tinh toan bng cach chuyn i tng tuyn tinh (I.27) s dung ham truyn g(.), tc la: zk = g(aj) (I.28) Kt xut cua mang c tinh bng cach chuyn i hoat ng cua cac nut n s dung mt lp cac nut th 2. Vi mi nut xut k, ta co: M (2) (2) a = w zj +w k i =1 kj k0 (I.29)

t z0 =1 ta co: M (2) a = w zj k i =0 kj (I.30)

Sau o gia tri nay c cho qua ham truyn phi tuyn cho ta kt xut u ra cua~ nut xut k: y k = g ( a k )

(I.31)

y ta s dung ki hiu biu din ham truyn cua cac nut xut nhm chi ra rng ham nay co th khng trung vi ham a c s dung trong lp n. Kt hp (I.27), (I.28), (I.30), (I.31) ta co cng thc chung cho m hinh mang trong hinh trn:

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~ M (2) d (1) y = g w g w ji xi k j =0 kj i =0

(I.32)

2.1.2.2 Kin trc mng tng qut Ta co th xy dng c nhng anh xa mang tng quat hn bng cach nghin cu nhng s mang phc tap hn. Tuy nhin y thi ta chi gii han nghin cu trong pham vi cac mang lan truyn tin. Mang lan truyn tin la mang khng co mt kt ni quay lui nao trong mang. Theo Bishop (1995): OV mt tng quat, mt mang c goi la lan truyn tin nu no co th gan cac s lin tuc cho tt ca cac nut nhp, tt ca cac nut n va nut xut sao cho mi nut chi co th nhn c cac kt ni t cac nut nhp hoc cac nut c gan s be hn.O Vi nhng mang co tinh cht nh th, kt xut cua mang la cac ham quyt inh cua cac u vao, va vi th toan b mang c goi la mt anh xa ham phi tuyn a bin. Kt xut cua nut k tinh c nh sau:

z = g w z j j kj k

(I.33)

trong o g(.) la mt ham truyn phi tuyn, va j thuc tp tt ca cac nut nhp va cac nut gi kt ni ti nut k (Tham s trong ngng cung a c bao ham trong tng nay). Vi mt tp cho trc cac gia tri u vao, ap dung lin tuc cng thc (I.33) se cho phep cac kich hoat cua tt ca cac nut trong mang c c lng, bao gm ca cac kich hoat cua cac nut xut. Qua trinh nay c goi la lan truyn tin cac tin hiu qua mang. Nu nh cac ham truyn cua tt ca cac nut n trong mang la tuyn tinh, thi vi nhng mang nh th ta lun lun tim c mt m hinh mang tng ng ma khng co mt nut n nao. Nhng mang nay c goi la mang tuyn tinh a lp va vi th khng

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c i su nghin cu, ma ngi ta chi chu yu nghin cu cac mang a lp vi cac ham truyn cua cac nut n la phi tuyn.

Hm sigmoidBy gi chung ta se xem xet ham truyn logistic dang S, trong o cac u ra cua no nm trong khoang (0,1), co phng trinh nh sau:

g( a) =

1 1 + exp( a )

(I.34)

Hinh ve di y biu din mt ham truyn sigmoid cho cac nut trong mang. y la mt ham mu co mt c tinh v cung quan trong vi : khi x chay t v cung ln n v cung be thi f(x) lun chay trong khoang t 0 n 1. Giai thut hoc y se iu chinh trong s cua cac kt ni gia cac nut ham nay anh xa gia tri cua x sang dang nhi phn, thng thng:

f(x) > 0.9 : f(x) = 1 f(x) < 0.1 : f(x) = 0.

Hnh 3: th hm truyn sigmoid

Trong phn nay chung ta se xem xet cac mang neural vi nut xut tuyn tinh. Tuy nhin iu nay cung chng han ch lp cac ham ma mang co th xp xi hoa. Vic s dung cac ham sigmoid tai cac u ra se gii han pham vi co th xay ra cua cac nut

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xut thanh pham vi co th at ti c cua ham sigmoid (gia tri kt xut la t 0 ti 1), va trong mt s trng hp thi iu nay co th la khng mong mun. Thm chi ngay ca khi gia tri xut mong mun la nm trong gii han cua ham sigmoid thi chung ta vn phai chu y rng ham sigmoid g(.) la mt ham n iu tng, do o no co th ly nghich ao c. Do vy mt gia tri xut y mong mun i vi mang co nut xut thuc dang sigmoid thi tng ng vi mt gia tri xut g-1(y) i vi mang co nut xut tuyn tinh. Mt nut n thuc dang sigmoid co th xp xi mt nut n tuyn tinh bt ki mt cach chinh xac. Cng vic nay at c bng cach thit k cho tt ca cac trong s cac cung u vao cua nut, cung nh cac trong ngng, sao cho rt nho ma tng cua cac gia tri nhp phai nm trn phn tuyn tinh cua ng cong sigmoid, gn ung vi ng thng nguyn thuy. Trong s trn cung xut t mt nut n tng cha cac nut k tip co th tao ra tng i ln tai ti l vi hoat ng (va vi trong ngng co c bc dich chuyn phu hp nu cn thit). Tng t, mt nut n dang sigmoid co th c tao ra nhm xp xi mt ham bc thang (step) bng vic t gia tri cho cac trong s va trong ngng rt ln. Bt ki mt anh xa ham lin tuc nao u co th c trinh bay vi chinh xac tuy y bi mt mang neural hai lp trong s s dung cac nut n dang sigmoid (Bishop, 1995). Do o chung ta bit c rng nhng mang neural vi nhiu tng nut x ly cung co kha nng xp xi hoa bi vi chung a cha ng trong no mang neural hai tng nh mt trng hp c bit. iu nay cho phep cac tng con lai c sp xp thc hin nhng bin i tuyn tinh nh a thao lun trn, va s bin i ng nht chinh la mt trng hp dc bit cua mt phep bin i tuyn tinh (bit rng co u s nut n khng co s giam bt v chiu xay ra).

Thut ton lan truyn ngcBy gi chung ta se tp trung nghin cu mt ki thut rt ph bin cua mang neural nhiu tng. Chung ta se xem xet cach ma mt mang hoc mt anh xa t mt tp d liu cho trc. Chung ta a bit vic hoc da trn inh nghia cua ham li, ham li nay sau o se c ti thiu hoa da vao cac trong s va cac trong ngng trong mang. Trc tin ta se xem xet trng hp mang s dung ham ngng. Vn cn ban y chinh la cach khi tao cac trong s cho mang nh th nao. Cng vic nay

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thng c goi la credit assignment problem. nu mt nut u ra tao ra mt ap s sai lch thi chung ta phai quyt inh xem liu nut n nao phai chiu trach nhim cho s sai lch o, cung chinh la vic quyt inh trong s nao cn phai iu chinh va iu chinh la bao nhiu. giai quyt vn gan trong s nay, chung ta hay xem xet mt mang vi cac ham truyn phn bit ,do o gia tri tng trong cua cac nut xut se tr thanh mt ham phn bit cua cac bin nhp va cua trong s va trong ngng. Nu ta coi ham li, vi du co dang sai s trung binh binh phng, la mt ham ring bit cho cac gia tri xut cua mang thi ban thn no cung chinh la mt ham phn bit cua cac trong s. Do o chung ta co th tinh toan c ao ham ham li theo cac trong s, va gia tri ao ham nay lai co th dung lam cc tiu hoa ham li bng cach s dung phng phap giam gradient (gradient descent) hoc cac phng phap ti u hoa khac. Giai thut c lng ao ham ham li c bit n vi tn goi lan truyn ngc, no tng ng vi vic lan truyn ngc li trong mang. Ki thut v lan truyn ngc c bit n rt rng rai va chi tit qua cac bai bao cung nh cac cun sach cua Rumelhart, Hinton va Williams (1986). Tuy nhin gn y mt s y tng tng t cung c mt s nha ngin cu phat trin bao gm Werbos (1974) va Parker (1985). Cn noi thm rng giai thut lan truyn ngc c s dung trong mang neural co y nghia rt ln. Vi du nh, kin truc cua mang perceptron nhiu tng cung thng c goi la mang lan truyn ngc. Khai nim lan truyn ngc cung thng c s dung m ta qua trinh hun luyn cua mang perceptron nhiu tng s dung phng phap gradient descent ap dung trn ham li dang sai s trung binh binh phng. lam ro hn v thut ng nay chung ta cn xem xet qua trinh luyn mang mt cach ki cang. Phn ln cac giai thut luyn mang u lin quan n mt thu tuc c lp i lp lai nhm lam ti thiu ham li, bng cach iu chinh trong s trong mt chui cac bc. Tai mi bc nh vy, chung ta co th chia thanh hai bc phn bit. Tai bc th nht, cn phai tinh ao ham ham li theo cac trong s. Chung ta a bit rng mt ong gop rt quan trong cua ki thut lan truyn ngc o la vic cung cp mt phng phap ht sc hiu qua v mt tinh toan trong vic anh gia cac ao ham. Vi tai bc nay li se c lan truyn ngc tr lai mang nn chung ta se s dung khai nim lan truyn ngc c trng ring cho vic anh gia ao ham nay.

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Tai bc th hai, cac ao ham se c s dung trong vic tinh toan s iu chinh i vi trong s. Va ki thut n gian nht c s dung y la ki thut gradient descent, ki thut nay c Rumelhart et al. (1986) a ra ln u tin. Mt iu ht sc quan trong la phai nhn thc c rng hai bc nay la phn bit vi nhau. Do o, qua trinh x ly u tin , c bit n la qua trinh lan truyn ngc cac li vao trong mang anh gia ao ham, co th c ap dung i vi rt nhiu laoi mang khac nhau ch khng chi i vi ring mang perceptron nhiu tng. No cung co th c ap dung vi cac loai ham li khac ch khng chi la ham tinh sai s binh phng cc tiu, va anh gia cac ao ham khac nay co th s dung cac phng phap khac nh phng phap ma trn Jacobian va Hessian ma chung ta se xem xet phn sau. Va cung tng t nh vy thi tai bc th hai, vic iu chinh trong s s dung cac ao ham a c tinh trc o co th thc hin vi nhiu phng phap ti u hoa khac nhau, va rt nhiu trong s cac phng phap o cho kt qua tt hn phng phap gradient descend. 2.1.4.1 Lan truyn ngc

Hnh 4: Lan truyn ngc

By gi chung ta se ap dung giai thut lan truyn ngc cho bt ki mt mang neural co cu hinh lan truyn tin tuy y, s dung cac ham truyn phi tuyn tuy y, va ca ham li co dang tuy y. minh hoa chung ta se dung mt mang co cu truc mt tng nut n dang sigmoid va ham li la ham tinh theo sai s trung binh binh phng.

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Trong cac mang lan truyn tin noi chung mi nut u tinh tng trong hoa cac u vao cua no theo cng thc:a j = w ji z ii

(I.35)

Vi zi la gia tri nhp hoc la gia tri xut cua mt nut co cung kt ni vi nut j va wji chinh la trong s cua cung kt ni o. Gia tri tng nay c tinh trn tt ca cac nut co kt ni trc tip vi nut j. Chung ta bit rng, trong ngng cua nut cung c a vao trong tng bng cach tao ra thm mt gia tri nhp c inh = 1. Tng trong (I.35) lai c bin i thng qua mt ham truyn phi tuyn g(.) a ra c gia tri xut zi cua nut j theo cng thc:z i = g ( a j ) (I.36)

By gi chung ta cn phai xac inh gia tri cua cac trong s trong mang thng qua vic ti thiu hoa ham li. y ta se coi ca ham li c vit nh mt tng cua tt ca cac li tai mi mu ring bit.Tng nay se c tinh trn tt ca cac mu cua tp hun luynE = Enn

(I.37)

Vi n la nhan cua tng mu. Chung ta cung gia inh rng li En co th c th hin nh mt ham ring cua cac bin u ra, co nghia la :

En = En(yc, , yc)

Muc ich cua chung ta y chinh la phai tim ra mt ham nhm tinh c ao ham cua ham li theo cac trong s va trong ngng cua mang. i vi tng mu, ta se coi nh a cung cp mt vector nhp tng ng la u vaova a tinh c cac gia tri xut cua cac nut n cung nh nut xut theo cac cng thc (I.35), (I.36). Qua trinh nay thng c goi la qua trinh lan truyn tin trong mang.

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By gi hay xem xet vic tinh ao ham cua En theo ca trong s wji. Gia tri xut cua cac nut se phu thuc vao tng mu nhp n nao. Tuy nhin d nhin, ta quy c se bo qua vic vit ki t n trn cac bin nhp va xut. Trc tin ta cn chu y rng En phu thuc vao trong s wji thng qua tng gia tri nhp ai cua nut j. Do o ta co th a ra cng thc tinh cac ao ham ring nh sau:

E n E n a j = * w ji a j w ji

(I.38)

T (I.35) ta c:

a j w ji

= zi

(I.39)

Nh vy suy ra:

n E = j z i ji w

(I.40)

Trong j

E n a j

T cng thc (I.40) ta thy rng tinh c ao ham chung ta chi cn tinh gia tri cho mi nut n va nut xut trong mang va sau o ap dung cng thc (I.40). Vi cac nut xut thi vic tinh k la ht sc n gian.

Ta c:k E n E n = g ' ( ak ) a k y k

(I.41)

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E n tnh ra (I.41) ta cn tm ra cng thc tnh g(a) v . y

tinh c cho ca nut n, ta cn s dung cng thc tinh ao ham ring:

j

E n E n a k = a j k a k a j

(I.42)

Trong o gia tri tng c tinh trn cac nut k ma nut j kt ni n. Vic sp xp cac nut cung nh cac trong s c minh hoa trong Hnh 6.

Hnh 5: Minh ha vic tnh j cho vic tnh nt n j

Chu y rng cac nut co nhan k nay co th bao gm ca nut nhp va nut xut. By gi chung ta co cng thc lan truyn ngc nh sau: j g ' ( a j ) wkj kk

(I.43)

Cng thc nay noi ln rng gia tri cua i vi mt nut n co th c tinh t vic lan truyn ngc cac gia tri cua cac nut n cao hn trong mang, nh c minh hoa trong hinh 5. Bi vi chung ta a bit c cac gia tri cua cac nut xut nn ta co th ap dung (I.43) mt cach quy nhm tinh ra cac gia tri cho tt ca cac nut n trong mang, ma khng quan tm n cu hinh cua no.

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Chung ta co th tng kt lai giai thut lan truyn ngc nhm tinh ao ham ham li En theo cac trong s trong 4 bc: a vector nhp xn vao mang va lan truyn tin no trong mang s dung va tim ra gia tri xut cho tt ca cac nut n cung nh nut xut. Tinh cho tt ca cac nut xut s dung cng thc Lan truyn ngc cac d bng cng thc thu c cho mi nut n trong mang.E n = j z i tinh cac ao ham. ap dung w ji

ao ham cua li tng E co th thu c bng cach lp i lp lai cac bc trn i vi trng mu trong tp hun luyn va sau o tinh tng trn tt ca cac li. Trong qua trinh tinh ao ham trn chung ta a gia inh rng mi nut n cung nh xut u co chung mt ham truyn g(.). Tuy nhin iu nay hoan toan co th tinh c vi trng hp mi nut khac nhau u co cac ham truyn ring, n gian bng cach anh du dang cua ham g(.) ng vi tng nut. 2.1.4.2 Hiu qu ca lan truyn ngc Mt trong nhng c tinh quan trong nht cua lan truyn ngc chinh la kha nng tinh toan hiu qua cua no. t w la tng s cac trong s va trong ngng. Do o mt phep tinh ham li (cho mt mu nhp nao o) cn O(w) thao tac vi w u ln. iu nay cho phep s lng trong s co th ln hn s lng nut, tr nhng mang co qua it kt ni. Do vy, hiu qua cua vic tinh toan trong lan truyn ngc se lin quan n vic tinh gia tri cua tng trong cng thc (I.35), con vic tinh toan cac ham truyn thi tng phi kha nho. Mi lt tinh tng trong (I.35) cn n mt phep nhn va mt phep cng, dn n chi phi tinh toan toan b se bng O(w). Vi tt ca w trong s thi se co w ao ham cn tinh toan. Vi mi ln tinh ao ham nh vy cn phai thc hin tim biu thc ham li, xac inh cng thc tinh ao ham va sau o tinh toan chung theo giai thut lan truyn ngc, mi cng vic o se oi hoi O(w) thao tac. Nh vy toan b qua trinh tinh toan tt ca cac ao ham se ti l vi O(w2). Giai tht lan truyn ngc cho phep cac ao ham c tinh trong O(w) thao tac. iu nay cung dn n rng ca hai pha lan truyn ngc va lan truyn tin u cn O(w) thao tac, vic tinh ao ham theo cng thc (I.43) cung cn O(w) thao tac.Nh vy giai thut lan truyn ngc a lam giam phc tap tinh toan t O(w2) n O(w) i vi mi

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vector nhp. Vi qua trinh luyn mang, du co s dung lan truyn ngc, co th cn rt nhiu thi gian, nn vic at c hiu qua nh vy la ht sc quan trong.Vi tng s N mu luyn, s lng cac bc tinh toan anh gia ham li trn toan b tp d liu se la N ln bc tinh toan cua mt mu.

Gii thiu v PCAPhn ny gip ngi c hiu c php phn tch thnh phn chnh (PCA). PCA l mt k thut hu ch trong cc ng dng nhn dng mt v nn nh, v l mt k thut ph bin tm mu trong cc d liu nhiu chiu[4]. Trc khi i vo tm hiu PCA, ti xin gii thiu v cc khi nim ton hc s c s dng trong PCA. Cc khi nim bao gm: lch chun (Standard deviation), phng sai (variance), hip phng sai (covariance), vec t ring (eigenvector), gi tr ring (eigenvalue).

Mt s khi nim ton hc2.2.1.1 lch chun hiu lch chun, chng ta cn mt tp d liu. Gi s ta c tp X = [1 2 4 6 12 15 25 45 68 67 65 98] X l k hiu i din cho tp s, mi s ring bit c k hiu Xi (V d X3 = 4). Phn t u tin l X1 v n l s lng phn t ca tp hp. Khi trung bnh ca mu c cng thc:

L k hiu trung bnh ca mu, tuy nhin trung bnh mu khng ni ln c nhiu iu ngoi tr cho ta bit n l mt im gia. V d vi 2 tp d liu [0 8 12 20] v [8 9 11 12]

c trung bnh mu bng nhau nhng li kh khc nhau. S khc bit y chnh l khong cch ca d liu. V lch chun l i lng o khong cch ny. Ta c th hiu lch chun l khong cch trung bnh t trung bnh mu n cc im ca d liu. Ta c cng thc:

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Tp hp 1

Tp hp 2

Ta c th d dng nhn thy tp d liu 1 c lch chun ln hn c khong cch ln hn tp d liu 2. 2.2.1.2 Phng sai Phng sai l mt i lng khc dng o khong cch ca d liu. Ta c cng thc:

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D thy phng sai chnh l bnh phng lch chun. 2.2.1.3 Hip phng sai Ta thy rng 2 i lng lch chun v phng sai ch s dng c trong 1 chiu. Trong thc t d liu c th c rt nhiu chiu. Mt v d n gin ta c d liu v cn nng v im s ca ton b sinh vin trong lp K51-KHMT. i vi d liu ny, lch chun v phng sai ch tnh c trn tng chiu ring bit v ta khng thy c mi lin h gia 2 chiu ny. Tng t phng sai, hip phng sai l i lng o s bin thin gia 2 chiu. Nu tnh hip phng sai gia 1 chiu vi chnh n ta c phng sai ca chiu . Nu tp d liu c 3 chiu x, y, z ta c th tnh hip phng sai ca tng cp chiu (x, y), (y, z), (z, x). Cng thc ca hip phng sai tng t cng thc ca phng sai. Cng thc ca phng sai c khai trin nh sau:

V cng thc ca hip phng sai:

T cng thc hip phng sai ta thy, nu dng th X, Y ng bin, m th X, Y nghch bin, nu bng 0 th X, Y c lp. 2.2.1.4 Ma trn hip phng sai Hip phng sai s bin thin gia 2 chiu, do i vi tp d liu c n chiu ta c gi tr hip phng sai khc nhau. V thun tin cho vic tnh ton ta biu din cc gi tr ny thng qua mt ma trn gi l ma trn hip phng sai. nh ngha ca ma trn nh sau:

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Trong l 1 ma trn vi n hng, n ct v Dimx l chiu th x. V d ma trn hip phng sai ca 1 tp d liu c 3 chiu x, y, z:

Ma trn i sPhn ny gii thiu v 2 khi nim l nn tng c s dng trong PCA l vect ring (eigenvector) v gi tr ring (eigenvalue).

Hnh 6: V d v 1 non-eigenvector v 1 eigenvector

Hnh 7: V d v 1 eigenvector c t l khc vn 1 l eigenvector

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Eigenvector (Vect ring)Ta c th nhn 2 ma trn vi iu kin kch c ph hp v eigenvector l 1 trng hp c bit ca php nhn ny. Quan st 2 php nhn ma trn vi vector trn hnh 3.1. v d th nht vect kt qu khng phi l mt bi s ca vect gc trong khi v d th 2 vect kt qu bng 4 ln vect gc. Ta thy rng vect (trong v d 2) biu din 1 mi tn t im (0, 0) n im (3, 2) v ma trn cn li c hiu l ma trn chuyn i. Nu ta nhn ma trn ny v bn tri ca vect th vect mi nhn c chnh l vect c b tnh tin i 1 lng. l tnh bin i ca vect ring. Cc tnh cht ca vect ring: Ch cc ma trn vung (n x n) mi c vect ring. Khng phi mi ma trn vung u c vect ring. Nu 1 ma trn vung (n x n) c vect ring th s c n vect ring. Nu nhn vect ring vi 1 s th kt qu sau khi nhn vi ma trn chuyn i, vect kt qu vn l vect ban u Tt c cc vect ring ca 1 ma trn u trc giao vi nhau

Eigenvalue (Gi tr ring)Gi tr ring l mt khi nim lin quan cht ch n vect ring. Thc t chng ta thy 1 gi tr ring trong hnh 3.1. Ch trong c 2 v d trn, s c nhn vi 2 vect ring bng nhau v bng 4. 4 c gi l gi tr ring ng vi 1 vect ring (2 vect ring trong 2 v d trn l tng ng nhau). Gi tr ring v vect ring lun i vi nhau thnh 1 cp.

0.1.1 Phn tch thnh phn chnh (PCA)PCA l 1 phng php nhn dng cc mu trong d liu v biu din d liu bng cch lm ni bt s ging v khc nhau. Khi cc mu trong d liu rt kh nhn ra trong khng gian nhiu chiu th PCA l mt cng c mnh phn tch chng.

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Cc bc c bn trong PCA: Bc1: Ly d liu (Get data) Bc2: Tr trung bnh mu. Vi mi chiu d liu gi s chiu x, ta u c 1 trung bnh mu, cng vic trong bc ny l tr tt c gi tr trong chiu x cho trung bnh mu x. Kt thc bc ny ta s c trung bnh mu tt c cc chiu l 0. Bc 3: Tnh ma trn hip phng sai Bc 4: Tnh cc vect ring v gi tr ring ca ma trn hip phng sai. Bc 5: Chn cc thnh phn chnh y l bc cui cng trong PCA. Trong bc ny, ty thuc vo s lng thnh phn chnh cn ly, ta ly ln lt cc thnh phn (vect ring) tng ng vi cc gi tr ring cao nht.

Chng 1. CC PHNG PHP NHN DNG CM XC KHUN MTTrong khun kh lun vn ny cc phng php nhn dng cm xc ch thc hin trn nh khun mt mu 2D.

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Nhn dng cm xc da trn PCA truyn thngTrch chn c trngXy dng mt tp cc vect c trng (S1, S2,Sk) cho mi hnh hun luyn s dng php phn tch PCA.

Hnh 8: V d v trch chn c trng bng PCA ng vi mi vect c trng ring c 1 gi tr ring. Nh vy mi hnh hun luyn c i din bi mt tp cc gi tr ring.

I = (b1, b2, b3 bn)

Mi cm xc bao gm 1 tp nh hun luyn V d cm xc vui I(Happy 1) = (bHappy 1 1, I(Happy 2) = (bHappy 2 1, : I(Happy m) = (bHappy m 1, bHappy m 2, bHappy m 3 bHappy m n) bHappy 1 2, bHappy 1 3 bHappy 1 n) bHappy 2 2, bHappy 2 3 bHappy 2 n)

Cm xc bun I(Sad 1) = (bSad 1 1, bSad 1 2, bSad 1 3 bSad 1 n)

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I(Sad 2) = (bSad 2 1, :

bSad 2 2, bSad 2 3 bSad 2 n)

I(Sad m) = (bSad m 1, bSad m 2, bSad m 3 bSad m n)

Vi 1 hnh nh cn nhn dng cm xc, s dng PCA ta c 1 tp cc gi tr ring. I(Nhan_dang) = (bNhan_dang 1, bNhan_dang 2, bNhan_dang 3 bNhan_dang n)

Qu trnh nhn dngLn lt tnh khong cch Euclid t nh cn nhn dng n mi nh trong tp hun luyn S(Happy 1) = (SHappy 1,1 - bNhan_dang 1)2+(SHappy 1,2 - bNhan_dang 2)2+...+(SHappy 1,n bNhan_dang n)2 S(Happy 2) = (SHappy 2,1 - bNhan_dang 2)2+(SHappy 2,2 - bNhan_dang 2)2+...+(SHappy 2,n bNhan_dang n)2 : S(Happy m) = (SHappy m,1 - bNhan_dang 1)2+(SHappy m,2 - bNhan_dang 2)2+...+(SHappy m,n bNhan_dang n)2 Khi cm xc ca nh cn nhn dng s c xc nh bng cm xc ca nh trong tp hun luyn m khong cch Euclid t nh n nh cn nhn dng l b nht.

Nhn dng cm xc da trn PCA kt hp cc thut ton hcMng nronM hnh:

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Hnh 9: M hnh mng nron Hnh v trn cho ta m hnh ca mng n ron s dng trong kha lun ny. y l mng MLP (MultiLayer Perceptron) bao gm 3 lp. Lp u vo gm 30 nt l 30 gi tr ring ca 1 nh sau khi dng PCA trch chn c trng. Lp n v lp u ra gm 5 nt l 5 cm xc. Trong m hnh mng neural MPL ny, chng ta s s dng thut ton lan truyn ngc (Backprobagation) tin hnh hc mng, phng php gim li c s dng l phng php gim gradient vi hm truyn hay hm kch hot l hm sigmoid. Ton b thut ton v l thuyt v vn ny c cp n trong chng I ca n.

Cy quyt nhTrong lnh vc hc my, cy quyt nh l mt kiu m hnh d bo (predictive model), ngha l mt nh x t cc quan st v mt s vt/hin tng ti cc kt lun v gi tr mc tiu ca s vt/hin tng. Mi mt nt trong (internal node) tng ng vi mt bin; ng ni gia n vi nt con ca n th hin mt gi tr c th cho bin . Mi nt l i din cho gi tr d on ca bin mc tiu, cho trc cc gi tr ca cc bin c biu din bi ng i t nt gc ti nt l . K thut hc my dng trong cy quyt nh c gi l hc bng cy quyt nh, hay ch gi vi ci tn ngn gn l cy quyt nh. Hc bng cy quyt nh cng l mt phng php thng dng trong khai ph d liu. Khi , cy quyt nh m t mt cu trc cy, trong , cc l i din cho cc phn loi cn cnh i din cho cc kt hp ca cc thuc tnh dn ti phn loi . Mt cy quyt nh c th c hc bng cch chia tp hp ngun thnh cc tp con da theo mt kim tra gi tr thuc tnh. Qu trnh ny c lp li mt cch qui cho mi tp con dn xut. Qu trnh qui hon thnh khi khng th tip tc thc hin vic chia tch c na, hay khi mt phn loi n c th p dng cho tng phn t ca tp con dn xut. Mt b phn loi rng ngu nhin (random forest) s dng mt s cy quyt nh c th ci thin t l phn loi. Cy quyt nh cng l mt phng tin c tnh m t dnh cho vic tnh ton cc xc sut c iu kin.

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Cy quyt nh c th c m t nh l s kt hp ca cc k thut ton hc v tnh ton nhm h tr vic m t, phn loi v tng qut ha mt tp d liu cho trc. D liu c cho di dng cc bn ghi c dng: (x, y) = (x1, x2, x3..., xk, y) Bin ph thuc (dependant variable) y l bin m chng ta cn tm hiu, phn loi hay tng qut ha. x1, x2, x3 ... l cc bin s gip ta thc hin cng vic .

Hnh 10: Cy quyt nh

Chng 2. THC NGHIMMi trng thc nghimChng trnh chy gii thut PCA c vit bng ngn ng Matlab, chy trn nn h iu hnh Windows 7 Professional, Laptop c tc CPU 2.0 Ghz, b nh ram 2Gb. Matlab l 1 phn mm ni ting ca cng ty MathWorks, l mt ngn ng hiu nng cao cho tnh ton k thut. N tch hp tnh ton, hin th v lp trnh trong mt mi trng d s dng. Mt s ng dng tiu biu ca Matlab nh: H tr ton hc v tnh ton, m phng, phn tch, kho st v hin th s liu, pht trin ng dng vi cc

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giao din ha. Matlab u tin c vit bng Fortran cung cp truy nhp d dng ti phn mm ma trn c pht trin bi cc d n Linpack v Eispack. Sau n c vit bng ngn ng C trn c s cc th vin nu trn v pht trin thm nhiu lnh vc ca tnh ton khoa hc v cc ng dng k thut. Ngoi cc tnh nng c bn, phn mm MATLAB cn c trang b thm cc ToolBox cc gi chng trnh (th vin) cho cc lnh vc ng dng rt a dng nh x l tn hiu, nhn dng h thng, x l nh, mng n ron, logic m, ti chnh, ti u ha, phng trnh o hm ring, tin sinh hc. Mng MultiLayer Perceptron c cung cp bi phn mm Weka.

D liu u voGm c 75 nh khun mt mu, phn gii 600 x 800 im nh, tt c cc nh u l khun mt ca mt ngi v c sng ng u nhau. Cm xc th hin trong mi nh kh r rng. Tp d liu ny ch c 5 cm xc chnh l: Vui, bun, gh tm, gin d v bnh thng.

Kho st v nh giTrong 75 nh khun mt mu, 40 nh bt k c chn lm d liu hun luyn v 35 nh cn li lm d liu test.

Phng php PCA truyn thngVi phng php ny, kt qu nhn dng c nh sau: Vui: 80% Gh tm: 70% Gin d: 86% Bun: 55% Bnh thng: 84% Trung bnh: 65%

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Da vo kt qu, ta c th thy kh nng nhn dng ca phng php ny khng cao. Tuy hin y l kt qu vi d liu hun luyn b. Nu tp d liu hun luyn ln hn chc chn kh nng nhn dng s tng. y l mt phng php n gin v d hiu nhn dng cm xc khun mt tuy nhin nhc im ln nht ca n l tc x l chm. Khi tp hun luyn ln, bao gm hng nghn nh, khi vi mi nh cn nhn dng, ta phi so khp vi ln lt tng nh trong tp hun luyn. V tc chm nn phng php ny thng khng c ng dng nhiu trong thc t. Bn cnh phng php ny cng gp kh khn khi nh cn nhn dng khng c sng tt, hoc khi khun mt khng chnh din.

Phng php s dng mng nronVi phng php hun luyn bng mng nron, chng ta s s dng gii thut Multilayer Perceptron c cung cp trong cng c Weka vi cc c trng c trch chn bng PCA, kt qu nhn dng c nh sau: Vui: 100 % Gh tm: 100% Gin d: 67% Bun: 50% Bnh thng: 80% Kt qu trung bnh: 87%

Khi thay i s tng n ln ln hn 5 hoc b hn 5, kt qu trung bnh gim xung 83,3%. Nh vy kh nng phn loi ca mng nron khng tng khi s lng tng n tng.

Phng php s dng cy quyt nhchng ta s s dng gii thut cy quyt nh J48-Decision Tree c cung cp trong Weka. Kt qu nhn dng c Vui: 60 % Gh tm: 14,3% Gin d: 16,7%

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trng

Bun: 0% Bnh thng: 60% Kt qu trung bnh: 36,7%

Trong gii thut cy quyt nh J48 c cung cp bi Weka c 3 tham s quan confidenceFactor: Nhn t s dng cho vic ct ta (Nu gi tr ny cng nh th cy sinh ra s c ct cng nhiu). minNumObj: S th hin ti thiu trn mt nt l trong cy. unPruned: nu l True th cy sinh ra s c ct ta v ngc li.

Sau khi iu chnh cc tham s, kt qu tt nht thu c: confidenceFactor: 0.25 minNumObj: 2 unPruned: False

Gii thut cy quyt nh J48 cho kt qu nhn dng rt thp, nguyn nhn c th do tp nh hun luyn qu t (45 nh).

2.1 Tng ktChng ny m t thc nghim v kt qu ca 3 phng php nhn dng cm xc. Phng php th nht l dng PCA v tnh khong cch Euclid, phng php ny kh nng nhn dng trung bnh, tc chm. Phng php th 2 s dng mng nron (MLP). Phng php ny c kh nng nhn dng tt, tc nhanh. Phng php th 3 l phng php dng cy quyt nh phn lp. Phng php ny t kt qu rt thp tuy nhin do tp nh hun luyn qu t (40 nh) nn cha nh gi ht kh nng nhn dng ca phng php ny. Trong c 3 phng php cm xc vui lun t kt qu cao nht do c s lng nh hun luyn nhiu nht. Cm xc bun c s lng nh hun luyn t nht nn t kt qu thp nht. Nhng cm xc cn li u t kt qu tng i.

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Cy quyt nh l mt gii thut phn lp nhng n ch t hiu qu cao khi s lng lp l 2. Vi s lng ln hn 2 tnh hiu qu ca gii thut gim i. Trong kha lun ny v thi gian chun b ngn nn khng i su vo phn tch gii thut ny m ch dng xem nh mt phng php tham kho thm.

Chng 3. KT LUNQua thi gian nghin cu v cc phng php nhn dng cm xc khun mt, c bit l qua qu trnh thc hin kha lun tt nghip, em tm hiu c mt s thut ton hc v p dng cc thut ton ny cho bi ton phn lp nhn dng cm xc. Nhng kt qu chnh m kha lun t c c th c tng kt nh sau: Gii thiu chi tit v phng php trch chn c trng (PCA) v Mng nron nhiu tng truyn thng (Multilayer Perceptron), ng thi gii thiu s lc v 1 gii thut phn lp khc l cy quyt nh. p dng cc gii thut ny cho bi ton nhn dng cm xc.

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Nhn xt v nh gi nhng kt qu t c ca cc gii thut trong bi ton nhn dng cm xc.

Bn cnh nhng kt qu t c, cn c nhng vn m thi im hin ti kha lun cha gii quyt c. Xy dng tp hun luyn ln t kt qu chnh xc hn. Nghin cu v mt s cc gii thut trch chn c trng v phn lp d liu khc Xy dng mt chng trnh hon chnh c giao din tng tc vi ngi s dng

PH LC - MT S THUT NG ANH VITThut ng Back propagation algorithm Cross validation Feed forward Input/hidden/output layer Mean squared error MLP (MultiLayer Perceptrons) Gii ngha Thut ton lan truyn ngc sai s Mt cch chn mu trong tp train v tp test trnh hin tng overfitting Lan truyn xui Lp u vo/n/ u ra Sai s bnh phng trung bnh Mng neuron nhiu tng truyn thng

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Transformation/activation function Unsupervised learning Validation set

Hm truyn/hm kch hot Hc khng c gim st Tp mu xc nhn mng

TI LIU THAM KHO1. G.Zhao, M.Pietikinen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007.

2. Y.L.Tian, T.Kanade, J.Cohn. Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001.

3. Z.Wen, T. Huang. Capturing Subtle Facial Motions in 3D Face Tracking. International Conference on Computer Vision. 2003.

4. Y.Zhang, Q.Ji. Active and dynamic information fusion for facial expression understanding from image sequence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005.

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5. M.S.Bartlett, J.C.Hager, P.Ekman, T.J.Sejnowski. Measuring facial expressions by computer image analysis. Psychophysiology. 1999.

6. Z.Zhang, M.Lyons, M.Schuster, S.Akamatsu. Comparison Between GeometryBased and Gabor-Wavelets-Based Facial Expression Recognition Using MultiLayer Perceptron. IEEE International Conference on Automatic Face and Gesture Recognition. 1998.

7. M.Pantic, I.Patras. Dynamics of facial expression: Recognition of facial actions and their temporal segments from face profile image sequences. IEEE Transactions on Systems, Man and Cybernetics. 2006.

8. E.Holden, R.Owens. Automatic Facial Point Detection, Asian Conference on Computer Vision. 2002.

9. D.Vukadinovic, M.Pantic. Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers. IEEE International Conference on Systems,Man and Cybernetics. 2005.

10. L.Chen, L.Zhang, H.Zhang, M.Abdel-Mottaleb. 3D Shape Constraint for Facial Feature Localization using Probabilistic-like Output. IEEE International Workshop Analysis and Modeling of Faces and Gestures. 2004.

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