PART 5 Supervised Hebbian Learning

PART 5 Supervised Hebbian Learning

Outline • Linear Associator • The Hebb Rule • Pseudoinverse Rule • Application

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 B A

Linear Associator

Hebb Rule(1/2)

Hebb Rule(2/2)

Batch Operation

Performance Analysis(1/2)

Performance Analysis(2/2)

Example(1/2)

Example(2/2)

Pseudoinverse Rule(1/2)

Pseudoinverse Rule(2/2)

Relationship to the Hebb Rule

Example

Autoassociative Memory

Tests 50% Occluded 67% Occluded Noisy Patterns (7 pixels)

Variations of Hebbian Learning Basic Rule: Learning Rate: Smoothing: Delta Rule: Unsupervised:

Solved Problems

Solved Problems Solution:

Solved Problems

PART 5 Supervised Hebbian Learning

PART 5 Supervised Hebbian Learning

Presentation Transcript

Supervised Hebbian Learning

Supervised Learning

Supervised learning

Supervised Learning

Semi-Supervised Learning

Supervised Learning

Assignment 3: Hebbian learning

Chapter 7 Supervised Hebbian Learning

Hebbian Coincidence Learning

Hebbian learning models

Supervised Learning

Hebbian Model Learning

Introduction Hebbian learning Generalised Hebbian learning algorithm Competitive learning

Self Organization: Hebbian Learning

WK7 – Hebbian Learning

Chapter 5: Partially-Supervised Learning

Supervised Learning

Supervised Learning

WK7 – Hebbian Learning

Supervised Learning