Pattern Recognition With Neural Networks In C
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Pattern Recognition With Neural Networks In C
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Author : Abhijit S. Pandya
language : en
Publisher: CRC Press
Release Date : 2020-10-12
Pattern Recognition With Neural Networks In C written by Abhijit S. Pandya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-12 with Computers categories.
The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks. Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary. C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.
Pattern Recognition With Neural Networks In C
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Author : Abhijit S. Pandya
language : en
Publisher: CRC Press
Release Date : 2019-12-17
Pattern Recognition With Neural Networks In C written by Abhijit S. Pandya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with categories.
The addition of artificial network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this practical guide to the application of artificial neural networks. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks.
Neural Networks For Pattern Recognition
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Author : Christopher M. Bishop
language : en
Publisher: Oxford University Press
Release Date : 1995-11-23
Neural Networks For Pattern Recognition written by Christopher M. Bishop and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-11-23 with Computers categories.
Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Pattern Recognition And Neural Networks
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Author : Brian D. Ripley
language : en
Publisher: Cambridge University Press
Release Date : 2007
Pattern Recognition And Neural Networks written by Brian D. Ripley and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.
Neural Networks And Deep Learning
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Author : Dr. Aakunuri Manjula
language : en
Publisher: Academic Guru Publishing House
Release Date : 2023-12-18
Neural Networks And Deep Learning written by Dr. Aakunuri Manjula and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-18 with Study Aids categories.
Neural networks are among the most aesthetically pleasing programming paradigms ever developed. The traditional programming approach involves providing the computer with instructions by decomposing complex problems into numerous smaller, well-defined tasks that can be executed effortlessly by the computer. In contrast, we do not instruct the computer on how to solve our problem within a neural network. Conversely, it acquires knowledge from empirical data and devises its own resolution to the given dilemma. Learning automatically from data appears promising. However, with the exception of a few specialized problems, we did not know how to train neural networks to outperform more conventional approaches until 2006. The year 2006 marked a turning point with the identification of methods for acquiring knowledge using deep neural networks. The current term for these methods is deep learning. Deep neural networks and deep learning have undergone significant advancements and now demonstrate exceptional performance across a wide range of critical issues in computer vision, speech recognition, and natural language processing. Major corporations including Google, Microsoft, and Facebook are implementing them extensively. This book aims to assist readers in mastering the fundamental concepts of neural networks, including contemporary deep learning techniques. Upon completing the book, readers will have developed code that solves complex pattern recognition problems using deep learning and neural networks. And they will have the knowledge and skills necessary to apply neural networks and deep learning to solve problems of their own design.
Neural Networks In Pattern Recognition And Their Applications
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Author : Chi Hau Chen
language : en
Publisher: World Scientific
Release Date : 1991-12-27
Neural Networks In Pattern Recognition And Their Applications written by Chi Hau Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-12-27 with Computers categories.
The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications.
Ram Based Neural Networks
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Author : James Austin
language : en
Publisher: World Scientific
Release Date : 1998
Ram Based Neural Networks written by James Austin and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Computers categories.
RAM-based networks are a class of methods for building pattern recognition systems. Unlike other neural network methods, they learn very quickly and as a result are applicable to a wide variety of problems. This important book presents the latest work by the majority of researchers in the field of RAM-based networks.
Neural Networks And Micromechanics
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Author : Ernst Kussul
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-12-01
Neural Networks And Micromechanics written by Ernst Kussul and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-01 with Computers categories.
Micromechanical manufacturing based on microequipment creates new possibi- ties in goods production. If microequipment sizes are comparable to the sizes of the microdevices to be produced, it is possible to decrease the cost of production drastically. The main components of the production cost - material, energy, space consumption, equipment, and maintenance - decrease with the scaling down of equipment sizes. To obtain really inexpensive production, labor costs must be reduced to almost zero. For this purpose, fully automated microfactories will be developed. To create fully automated microfactories, we propose using arti?cial neural networks having different structures. The simplest perceptron-like neural network can be used at the lowest levels of microfactory control systems. Adaptive Critic Design, based on neural network models of the microfactory objects, can be used for manufacturing process optimization, while associative-projective neural n- works and networks like ART could be used for the highest levels of control systems. We have examined the performance of different neural networks in traditional image recognition tasks and in problems that appear in micromechanical manufacturing. We and our colleagues also have developed an approach to mic- equipment creation in the form of sequential generations. Each subsequent gene- tion must be of a smaller size than the previous ones and must be made by previous generations. Prototypes of ?rst-generation microequipment have been developed and assessed.
Advances In Pattern Recognition Systems Using Neural Network Technologies
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Author : Patrick S P Wang
language : en
Publisher: World Scientific
Release Date : 1994-01-01
Advances In Pattern Recognition Systems Using Neural Network Technologies written by Patrick S P Wang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-01-01 with categories.
Contents:A Connectionist Approach to Speech Recognition (Y Bengio)Signature Verification Using a “Siamese” Time Delay Neural Network (J Bromley et al.)Boosting Performance in Neural Networks (H Drucker et al.)An Integrated Architecture for Recognition of Totally Unconstrained Handwritten Numerals (A Gupta et al.)Time-Warping Network: A Neural Approach to Hidden Markov Model Based Speech Recognition (E Levin et al.)Computing Optical Flow with a Recurrent Neural Network (H Li & J Wang)Integrated Segmentation and Recognition through Exhaustive Scans or Learned Saccadic Jumps (G L Martin et al.)Experimental Comparison of the Effect of Order in Recurrent Neural Networks (C B Miller & C L Giles)Adaptive Classification by Neural Net Based Prototype Populations (K Peleg & U Ben-Hanan)A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes: A Pilot Study (L Wiskott & C von der Malsburg)and other papers Readership: Computer scientists and engineers.
Neural Networks And Statistical Learning
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Author : Ke-Lin Du
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-09
Neural Networks And Statistical Learning written by Ke-Lin Du and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-09 with Technology & Engineering categories.
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.