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% Train a two layer neural network with the Levenberg-Marquardt % method. % % If desired, it is p

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% Train a two layer neural network with the Levenberg-Marquardt % method. % % If desired, it is possible to use regularization by % weight decay. Also pruned (ie. not fully connected) networks can % be trained. % % Given a set of corresponding input-output pairs and an initial % network, % [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) % trains the network with the Levenberg-Marquardt method. % % The activation functions can be either linear or tanh. The % network architecture is defined by the matrix NetDef which % has two rows. The first row specifies the hidden layer and the % second row specifies the output layer.

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