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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Classification using the backpropagation algorithm

The backpropagation (BP) algorithm learns the classification model by training a multilayer feed-forward neural network. The generic architecture of the neural network for BP is shown in the following diagrams, with one input layer, some hidden layers, and one output layer. Each layer contains some units or perceptron. Each unit might be linked to others by weighted connections. The values of the weights are initialized before the training. The number of units in each layer, number of hidden layers, and the connections will be empirically defined at the very start.

Classification using the backpropagation algorithm

The training tuples are assigned ...

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