Upload
ginata
View
170
Download
3
Embed Size (px)
DESCRIPTION
Chapter 7 Supervised Hebbian Learning. Outline. Linear Associator The Hebb Rule Pseudoinverse Rule Application. Linear Associator. Hebb ’ s Postulate. - PowerPoint PPT Presentation
Citation preview
Hebb’s Postulate“When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.”
D. O. Hebb, 1949
A
B
Pseudoinverse Rule(3/3) is Moore-Penrose Pseudoinverse. The Pseudoinverse of a real matrix P is the uniqui matrix that satisfies
Example
p1
1–11–
t1 1–= =
p2
111–
t2 1= =
W TP+1– 1
1– 11 11– 1–
+
= =
P+ PTP 1–PT 3 1
1 3
1–1– 1 1–1 1 1–
0.5– 0.25 0.25–0.5 0.25 0.25–
= = =
W T P+1– 1
0.5– 0.25 0.25–0.5 0.25 0.25–
1 0 0= = =
Wp1 1 0 01–11–
1–= = Wp2 1 0 0111–
1= =
Variations of Hebbian Learning
Wnew Wold tqpqT
+=
Wnew Wold tqpq
T+=
Wnew Wold tqpq
TWold
–+ 1 – Woldtqpq
T+= =
Wnew Wol d tq aq– pqT+=
Wnew Wold aqpqT+=
Basic Rule:
Learning Rate:
Smoothing:
Delta Rule:
Unsupervised: