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Journal ArticleDOI

Pattern recognition with neural networks combined by genetic algorithm

Sung-Bae Cho
- 16 Apr 1999 - 
- Vol. 103, Iss: 2, pp 339-347
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TLDR
The experimental results with the recognition problem of totally unconstrained handwritten numerals show that the genetic algorithm produces better results than the conventional methods such as averaging and Borda count.
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This article is published in Fuzzy Sets and Systems.The article was published on 1999-04-16. It has received 69 citations till now. The article focuses on the topics: Population-based incremental learning & Artificial neural network.

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Citations
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MonographDOI

Combining Pattern Classifiers

TL;DR: This combining pattern classifiers methods and algorithms helps people to enjoy a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.
Book ChapterDOI

Computational Intelligence: An Introduction

TL;DR: The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.
Journal ArticleDOI

Classifier selection for majority voting

TL;DR: This work provides a revision of the classifier selection methodology and evaluates the practical applicability of diversity measures in the context of combining classifiers by majority voting, and proposes a novel design of multiple classifier systems in which selection and fusion are recurrently applied to a population of best combinations of classifiers.

On the Design of

TL;DR: The Division of Design educates and trains designers to create and develop concepts that optimize the function, value, and appearance of communications, products, and systems for the benefit of both industry and society.
Journal ArticleDOI

Multiple classifier decision combination strategies for character recognition: A review

TL;DR: This paper explicitly reviews the field of multiple classifier decision combination strategies for character recognition, from some of its early roots to the present day and illustrates explicitly how the principles underlying the application of multi-classifier approaches to character recognition can easily generalise to a wide variety of different task domains.
References
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Journal ArticleDOI

Neural network ensembles

TL;DR: It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks, which helps improve the performance and training of neural networks for classification.
Proceedings Article

Handwritten Digit Recognition with a Back-Propagation Network

TL;DR: Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task, and has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.
Journal ArticleDOI

Methods of combining multiple classifiers and their applications to handwriting recognition

TL;DR: On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.
Journal ArticleDOI

Genetic algorithms: a survey

TL;DR: The analogy between genetic algorithms and the search processes in nature is drawn and the genetic algorithm that Holland introduced in 1975 and the workings of GAs are described and surveyed.
Book

Fuzzy logic, neural networks, and soft computing

TL;DR: A simple case in point is the problem of parking a car as discussed by the authors, where the final position of the car is not specified exactly, and if it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position.