Proceedings ArticleDOI
Feature extraction in the Neocognitron
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TLDR
The authors show that the feature extraction process is equivalent to a generalized nonlinear discriminant and that the operation of the feature-extraction process can be linked to the eigenvectors and eigenvalues of a matrix comprised of the excitatory and inhibitory convolution masks.Abstract:
The authors present theoretical and numerical developments in the understanding of feature extraction in the Neocognitron. First, they show that the feature extraction process is equivalent to a generalized nonlinear discriminant. Second, they show that the operation of the feature-extraction process can be linked to the eigenvectors and eigenvalues of a matrix comprised of the excitatory and inhibitory convolution masks. Third, the authors show how the choice of parameters for the feature extraction and learning process affects the feature extraction capabilities of the machine. >read more
Citations
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Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position
Kunihiko Fukushima,Sei Miyake +1 more
TL;DR: The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.
Journal ArticleDOI
The digi-neocognitron: a digital neocognitron neural network model for VLSI
B.A. White,M.I. Elmasry +1 more
TL;DR: The new model, the digi-neocognitron (DNC), has the same pattern recognition performance as the NC and has substantial advantages over the NC model for VLSI implementation.
Journal ArticleDOI
Shift invariance and the neocognitron
TL;DR: It is shown how certain model parameters must be chosen appropriately to obtain approximate shift invariance, and how these parameters should be chosen to reach a compromise between invariance and classification sensitivity.
Journal Article
An evaluation of the neocognitron
TL;DR: Tests of Fukushima's original system and the novel systems proposed in this paper suggest that it may be difficult for the neocognitron to achieve the performance of existing digit classifiers due to its reliance upon the supervisor's choice of selectivity parameters and training data.
Journal ArticleDOI
An evaluation of the neocognitron
TL;DR: In this article, a sequence of experiments investigating the strengths and limitations of Fukushima's neocognitron as a handwritten digit classifier is described. But, the performance is strongly dependent on the choice of selectivity parameters and two methods to adjust these variables are presented.
References
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Journal ArticleDOI
Neocognitron: A Self Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position
TL;DR: A neural network model for a mechanism of visual pattern recognition that is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity of their shapes without affected by their positions.
Journal ArticleDOI
Neocognitron: A neural network model for a mechanism of visual pattern recognition
TL;DR: In this article, a large-scale network with a learning-with-a-teacher (L2Teacher) process is used for reinforcement of the modifiable synapses in the new large-size model, instead of the learning-without-a teacher process applied to a previous model.
Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position
Kunihiko Fukushima,Sei Miyake +1 more
TL;DR: The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.
Journal ArticleDOI
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
Kunihiko Fukushima,Sei Miyake +1 more
TL;DR: In this paper, a multilayered network consisting of neuron-like cells is proposed for pattern recognition, which is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes.
Journal ArticleDOI
A New Approach to Pattern Recognition
TL;DR: Automatic clustering methods are shown to provide a basis for adaptive pattern recognition when neither the number nor the specifications of classes are known in advance.
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Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
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