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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
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DEEP LEARNING TECHNIQUES: CLUSTER ANALYSIS and PATTERN RECOGNITION with NEURAL NETWORKS. Examples with MATLAB
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Pattern Recognition with Neural Networks in C++
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A probabilistic neural network (PNN) is a sort of feedforward neural network used to handle classification and pattern recognition problems. In the PNN technique, the parent probability distribution function (PDF) of each class is approximated using a Parzen window and a non-parametric function.
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Real-Time Anomaly Detection for Cognitive Intelligence with deep learning and Cognitive Computing along with their Use Cases.
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Despite decades of research, scientists have yet to create an artificial neural network capable of rivaling the speed and accuracy of the human visual cortex. Now, Haizhou Li and Huajin Tang at the A*STAR Institute for Infocomm Research and co-workers in Singapore propose using a spiking neural network (SNN) to solve real-world pattern recognition problems. Artificial neural networks capable of such pattern recognition could have broad applications in biometrics, data mining and image analysis.
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The Deep Learning Market, 2020-2030 report features an extensive study of the current market landscape and the likely adoption of deep learning in healthcare…
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This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence.Product DetailsISBN-13: 9783642063251 Publisher: Springer Berlin Heidelberg Publication Date: 11-25-2010 Pages: 272 Product Dimensions: 6.10(w) x 9.25(h) x 0.02(d) Series: Studies in Fuzziness and Soft Computing #172
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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. • Author: Abhijit S Pandya • ISBN:9780849394621 • Format:Hardcover • Publication Date:1995-10-01
