Practical neural network recipes in C++
Masters, Timothy.
Practical neural network recipes in C++ - Boston : Academic Press, c1993. - xviii, 493 p. : ill. ; 1 computer disk (5 1/4 in.)
System requirements for computer disk: PC; C++ compiler. Included Index.
Foundations. Classification. Autoassociation. Time Series Prediction. Function Approximation. Multilayer Feedforward Networks. Eluding Local Minimai: Simulated Annealing. Eluding Local Minima II: Genetic Optimisation. Regression and Neural Networks. Designing Feedforward Network Architectures. Interpreting Weights: How Does This Thing Work? Probalistic Neural Networks. Functional Link Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Confidence Measures. Optimizing the Decision Threshold. Using the NEURAL Program. Appendix. Bibliography. Index.
A handbook for neural network solutions to practical problems using C++, providing guidance on designing the training set, preprocessing variables, training and validating the network and evaluating its performance. The IBM diskette includes the source code for all the programs in the book.
0124790402 (alk. paper) 9780124790407
Neural networks (Computer science)
C++ (Computer program language)
005.133 / MAS
Practical neural network recipes in C++ - Boston : Academic Press, c1993. - xviii, 493 p. : ill. ; 1 computer disk (5 1/4 in.)
System requirements for computer disk: PC; C++ compiler. Included Index.
Foundations. Classification. Autoassociation. Time Series Prediction. Function Approximation. Multilayer Feedforward Networks. Eluding Local Minimai: Simulated Annealing. Eluding Local Minima II: Genetic Optimisation. Regression and Neural Networks. Designing Feedforward Network Architectures. Interpreting Weights: How Does This Thing Work? Probalistic Neural Networks. Functional Link Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Confidence Measures. Optimizing the Decision Threshold. Using the NEURAL Program. Appendix. Bibliography. Index.
A handbook for neural network solutions to practical problems using C++, providing guidance on designing the training set, preprocessing variables, training and validating the network and evaluating its performance. The IBM diskette includes the source code for all the programs in the book.
0124790402 (alk. paper) 9780124790407
Neural networks (Computer science)
C++ (Computer program language)
005.133 / MAS