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Supervised Learning Extensions to the CLAM Network.

PATRICK COURTNEY¹, IAN ABRAHAM, NEIL A. THACKER

¹ITMI Aptor, UK

Neural Networks : the Official Journal of the International Neural Network Society

|March 1, 1997

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Contextual Layered Associative Memory (CLAM) offers flexible, unsupervised learning for probabilistic classification. Its self-generating structure supports adaptable layered mappings, outperforming traditional methods.

Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

The Contextual Layered Associative Memory (CLAM) is a novel self-generating structure.
It utilizes a probabilistic encoding scheme for associative mapping.

Purpose of the Study:

To demonstrate CLAM's capability in approximating conditional probabilities for classification.
To highlight the system's flexible learning approach compared to conventional methods.

Main Methods:

Development of unsupervised training algorithms for layerable associative mapping.
Training of CLAM layers to approximate conditional probabilities.
Independent operation of unsupervised and supervised learning algorithms.

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Main Results:

CLAM structures successfully support layered training for probabilistic classification outputs.
The unsupervised representational layer can be developed independently of supervision.
The system demonstrates enhanced flexibility over conventional node labeling schemes.

Conclusions:

CLAM provides a flexible framework for machine learning, particularly in classification tasks.
Its architecture supports adaptable, layered associative mappings through probabilistic encoding.
The independent learning phases enhance the system's adaptability and robustness.