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

There is one biological neural network, which has not received the attention it deserves from mainstream science. Ripley provides with each other two vital tips in sample recognition: statistical approaches and device understanding by means of neural networks. The system was successful in classifying all the input vectors into near drowning and drowning classes. This network is modular and is repeatedly utilized throughout the brain. Artificial neural network classification of NMR spectra of plant extracts. Matlab's Neural Network Pattern Recognition Tool Box was used to process the data. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. The ability of Neural Networks to solve complex problems in control, system identification, signal processing, communication, pattern recognition, etc. Neural Network based Pattern Recognition (Fingerprint). Neural networks are used for modeling complex relationships between inputs and outputs or to find patterns in data. Yampolskiy's main areas of interest are behavioral biometrics, digital forensics, pattern recognition, genetic algorithms, neural networks, artificial intelligence and games. Special-Purpose Architectures, Software and Hardware Tools Supporting Information Technologies for Pattern Recognition, Image, Speech and Signal Processing, Analysis and Understanding. This is a modified Self-Organizing Map designed specifically to learn fingerprints and can be used for fingerprint based verification and authentication.