Multi-class pattern classification using neural networks
Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and co...
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| Vydané v: | Pattern recognition Ročník 40; číslo 1; s. 4 - 18 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Oxford
Elsevier Ltd
2007
Elsevier Science |
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and
P-against-
Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data. |
|---|---|
| AbstractList | Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and
P-against-
Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data. |
| Author | Ou, Guobin Murphey, Yi Lu |
| Author_xml | – sequence: 1 givenname: Guobin surname: Ou fullname: Ou, Guobin – sequence: 2 givenname: Yi Lu surname: Murphey fullname: Murphey, Yi Lu email: yilu@umich.edu |
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| Keywords | Multi-class classification Pattern recognition Neural networks Machine learning System architecture Network architecture Neural network Object recognition Modeling Implementation Learning Coding Pattern classification Object detection Speech recognition Open market Time complexity Speech processing Target detection |
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