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Learning as the Evolution of Representation

Learning as the Evolution of Representation

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Excerpt from Learning as the Evolution of Representation: Technical Report 477, November 1898

A widely accepted approach to the formal modelling of learning is to view it as a process of constructing high-level structures that allow compression of raw data. The main results of this thesis are a number of precise formulation of this process, and a few algorithms that carry it out.

Our rigorous definitions allow us to identify of a novel concept of structure: the alphabet. We propose that a learning mechanism is one which constructs such structures as representations of the given data. The alphabet allows to draw interesting links between theories in pattern recognition and pattern classification.

The structural descriptions constructed by the algorithms are based on the two concepts of code and classification used, as data compression tools. We provide means for evaluating the efficiency of the encodings created using ideas from information theory. We reduce learning to an optimization problem and we suggest that the mechanisms proposed work at any descriptive level.

Our approach was inspired by the goal of interpreting learning as the natural evolution of a physical system to its ground states.

The applications discussed include an architecture and a learning rule for neural nets as well as a procedure for grammatical inference. Some of the ideas proposed have been tested on meaningful examples.

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