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The "MultiLayerPerceptron" page has been changed by YexiJiang: http://wiki.apache.org/hama/MultiLayerPerceptron?action=diff&rev1=16&rev2=17 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. + + One step of feed-forward is illustrated in the below figure. + + {{https://docs.google.com/drawings/d/1hJ2glrKKIWokQOy6RI8iw1T8TmuZFcbaCwnzGoKc8gk/pub?w=586&h=302}} + + For each layer except the input layer, the value of the current neuron is calculated by taking the linear combination of the values output by the neurons of the previous layer, where the weight determines the contribution of a neuron in the previous layer to current neuron. Obtaining the linear combination result z, a non-linear squashing function is used to constrain the output into a restricted range. Typically, sigmoid function or tanh function are used. + + For each step of feed-forward, the calculated results are propagated one layer close to the output layer. + To be added...
