We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We deri ve a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended.

He is now at the University of California, Berkeley.

The HN calculates the Hamming distance between the input pattern and each memory pattern, and selects the memory with the smallest distance. It is composed of two subnets: The similarity subnet, consisting of an n-neuron input layer connected with an m-neuron memory layer, calculates the number of equal bits between the input and each memory pattern. The winner-take-all (WTA) subnet, consisting of a fully connected m-neuron topology, selects the memory neuron that best matches the input pattern.

Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple distances (Euclidean, dot product...), which are not particularly meaningful on pattern vectors. More complex, better suited distance measures are often expensive and rather ad-hoc (elastic matching, deformable templates). We propose a new distance measure which (a) can be made locally invariant to any set of transformations of the input and (b) can be computed efficiently. We tested the method on large handwritten character databases provided by the Post Office and the NIST. Using invariances with respect to translation, rotation, scaling, shearing and line thickness, the method consistently outperformed all other systems tested on the same databases.

Memory-based classification algorithms such as radial basis functions orK-nearest neighbors typically rely on simple distances (Euclidean, dotproduct ...), which are not particularly meaningful on pattern vectors. More complex, better suited distance measures are often expensive and rather ad-hoc (elastic matching, deformable templates). We propose a new distance measure which (a) can be made locally invariant to any set of transformations of the input and (b) can be computed efficiently. We tested the method on large handwritten character databases provided by the Post Office and the NIST. Using invariances with respect to translation, rotation, scaling,shearing and line thickness, the method consistently outperformed all other systems tested on the same databases.

We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We deri ve a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended.

He is now at the University of California, Berkeley.

Hand-printed digits can be modeled as splines that are governed by about 8 control points.

We have investigated the properties of neurons in inferior temporal (IT) cortex in monkeys performing a pattern matching task. Simple backpropagation networks were trained to discriminate the various stimulus conditions on the basis of the measured neuronal signal. We also trained networks to predict the neuronal response waveforms from the spatial patterns of the stimuli. The results indicate t.hat IT neurons convey temporally encoded information about both current and remembered patterns, as well as about their behavioral context.

We have investigated the properties of neurons in inferior temporal (IT) cortex in monkeys performing a pattern matching task. Simple backpropagation networks were trained to discriminate the various stimulus conditions on the basis of the measured neuronal signal. We also trained networks to predict the neuronal response waveforms from the spatial patterns of the stimuli. The results indicate t.hat IT neurons convey temporally encoded information about both current and remembered patterns, as well as about their behavioral context.