The current generation of non-modular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks'...
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ISBN:
(纸本)0780341236
The current generation of non-modular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks' hidden nodes. We propose the Cooperative Modular Neural Network (CMNN) architecture, which deals with different levels of overlap in different modules. The modules share their information and cooperate in taking a global classification decision through voting. Moreover, special modules are dedicated to resolve high overlaps in the input-space. The performance of the new model outperforms that of the nonmodular alternative when when applied to ten famous benchmark classification problems.
The current generation of nonmodular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks'...
详细信息
The current generation of nonmodular neural network classifiers is unable to cope with classification problems which have a wide range of overlap among classes. This is due to the high coupling among the networks' hidden nodes. We propose the Cooperative Modular Neural Network (CMNN) architecture, which deals with different levels of overlap in different modules. The modules share their information and cooperate in taking a global classification decision through voting. Moreover, special modules are dedicated to resolve high overlaps in the input-space. The performance of the new model outperforms that of the nonmodular alternative when when applied to ten famous benchmark classification problems.
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