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The "MultiLayerPerceptron" page has been changed by YexiJiang: http://wiki.apache.org/hama/MultiLayerPerceptron?action=diff&rev1=15&rev2=16 Node: This page is always under construction. == What is Multilayer Perceptron? == - A [[http://en.wikipedia.org/wiki/Multilayer_perceptron|multilayer perceptron]] 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 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. In a multilayer perceptron, the neurons are aligned in layers and in any two adjacent layers the neurons are connected in pairs with weighted edges. - A practical multilayer perceptron consists of at least three layers of neurons, including one input layer, one or more hidden layers, and one output layer. + A practical multilayer perceptron consists of at least three layers of neurons, including one input layer, one or more hidden layers, and one output layer. + + 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. Here is an example multilayer perceptron with 1 input layer, 1 hidden layer and 1 output layer: @@ -19, +22 @@ == How Multilayer Perceptron works? == - In general, people use the (already prepared) MLP by feed the input feature to the input layer and get the result from the output layer. + In general, people use the (already prepared) MLP by feeding the input feature to the input layer and get the result from the output layer. + The results are calculated in a feed-forward approach, from the input layer to the output layer. + To be added...
