Neural Networks for Pattern Recognition. Christopher M. Bishop

Neural Networks for Pattern Recognition


Neural.Networks.for.Pattern.Recognition.pdf
ISBN: 0198538642,9780198538646 | 498 pages | 13 Mb


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Neural Networks for Pattern Recognition Christopher M. Bishop
Publisher: Oxford University Press, USA




Webb (2002) Statistical Pattern Recognition. Neural Network based Pattern Recognition (Fingerprint). Artificial neural network classification of NMR spectra of plant extracts. Pattern Recognition and Neural Networks (Ripley). The task that neural networks accomplish very well is pattern recognition. Obtained by studying the physics of the problem. Learning in biological systems involves adjustments to the Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. At present, artificial neural networks are emerging as the technology of choice for many applications, such as pattern recognition, prediction, system identification, and control. Lateral neural networking structures may hold the key to accurate artificial vision, pattern recognition, and image identification. Statistical Pattern Recognition (Webb). This is a modified Self-Organizing Map designed specifically to learn fingerprints and can be used for fingerprint based verification and authentication. Particularly good for performance measures and feature selection. You communicate a pattern to a neural network and it communicates a pattern back to you.