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The "MultiLayerPerceptron" page has been changed by YexiJiang: http://wiki.apache.org/hama/MultiLayerPerceptron?action=diff&rev1=17&rev2=18 Node: This page is always under construction. == What is Multilayer Perceptron? == - A [[http://en.wikipedia.org/wiki/Multilayer_perceptron|multilayer perceptron (MLP)]] is a kind of feed forward [[http://en.wikipedia.org/wiki/Artificial_neural_network|artificial neural network]], which is a mathematic model inspired by the biological neural network. + A [[http://en.wikipedia.org/wiki/Multilayer_perceptron|multilayer perceptron (MLP)]] is a kind of Too feed forward [[http://en.wikipedia.org/wiki/Artificial_neural_network|artificial neural network]], which is a mathematic model inspired by the biological neural network. The multilayer perceptron can be used for various machine learning tasks such as classification and regression. The basic component of a multilayer perceptron is the neuron. @@ -13, +13 @@ The size of input layer and output layer determines what kind of data a MLP can accept. Specifically, the number of neurons in the input layer determines the dimensions of the input feature, the number of neurons in the output layer determines the dimension of the output labels. Typically, the two-class classification and regression problem requires the size of output layer to be one, while the multi-class problem requires the size of output layer equals to the number of classes. + As for hidden layer, the number of neurons is a design issue. If the neurons are too few, the model will not be able to learn complex decision boundaries. On the contrary, too many neurons will decrease the generalization of the model. Here is an example multilayer perceptron with 1 input layer, 1 hidden layer and 1 output layer:
