Comparing Combination Rules of Pairwise Neural Networks Classifiers
Neural Processing Letters
Multiclass classification using neural networks and interval neutrosophic sets
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Fault diagnostics in electric drives using machine learning
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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Multiclass neural learning involves finding appropriate neural network architecture, encoding schemes, learning algorithms, etc. In this paper, we discuss major approaches used in neural networks for classifying multiple classes. The discussion is focused d on these architectures using either a system of multiple neural networks or a single neural network. We will discuss various learning algorithms, One-Again-All, One-Against-One, and P-against-Q. We will also discuss training procedures associated with each approach, implementation and time complexity. These methods are evaluated though their performances on the NIST handwritten digit database.