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- W1567662521 abstract "This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues." @default.
- W1567662521 created "2016-06-24" @default.
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- W1567662521 date "1993-01-01" @default.
- W1567662521 modified "2023-10-03" @default.
- W1567662521 title "Neural Networks for Pattern Recognition" @default.
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- W1567662521 doi "https://doi.org/10.1016/s0065-2458(08)60404-0" @default.
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