Journal ArticleDOI
Minimum distance automata in parallel networks for optimum classification
J. H. Winters,Christopher Rose +1 more
TLDR
A parallel implementation of the optimum (or maximum likelihood Gaussian) classifier that uses a cellular automaton to very rapidly find the output vector with minimum Euclidean distance from the input vector is presented.About:
This article is published in Neural Networks.The article was published on 1989-03-01. It has received 30 citations till now. The article focuses on the topics: Euclidean distance & Cellular automaton.read more
Citations
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Journal ArticleDOI
Competitive learning algorithms for vector quantization
TL;DR: A new competitive-learning algorithm based on the “conscience” learning method is introduced that is shown to be efficient and yields near-optimal results in vector quantization for data compression.
Journal ArticleDOI
Electrical signal processing techniques in long-haul fiber-optic systems
TL;DR: The results for a simulated binary 8-Gb/s system show that simple techniques can be used to reduce intersymbol interference substantially, thereby increasing the system margin by several decibels.
Journal ArticleDOI
Neural networks for vector quantization of speech and images
TL;DR: The authors show how a collection of neural units can be used efficiently for VQ encoding, with the units performing the bulk of the computation in parallel, and describe two unsupervised neural network learning algorithms for training the vector quantizer.
Journal ArticleDOI
Fuzzy competitive learning
Fu-Lai Chung,Tong Lee +1 more
TL;DR: The proposed fuzzy algorithms consist of various distinctive features such as converging more often to the desired solutions, or equivalently, reducing the likelihood of neuron underutilization that has long been a major shortcoming of crisp competitive learning.
Journal ArticleDOI
Comparative performance of the FSCL neural net and K-means algorithm for market segmentation
TL;DR: It is observed that a combination of the two methodologies, wherein the results of the FSCL network are input as seeds to the K-means, seems to provide more managerially insightful segmentation schemes.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
Neurons with graded response have collective computational properties like those of two-state neurons.
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
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Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal Article
Vector quantization
TL;DR: During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes.