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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
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Pattern Recognition with Neural Networks in C++
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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.
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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
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Learning sequential data with the help of linear systems.- A spiking neural network for personalised modelling of Electrogastogrophy (EGG).- Improving generalization abilities of maximal average margin classifiers.- Finding small sets of random Fourier features for shift-invariant kernel approximation.- Incremental construction of low-dimensional data representations.- Soft-constrained nonparametric density estimation with artificial neural networks.- Density based clustering via dominant sets.- Co-training with credal models.- Interpretable classifiers in precision medicine: feature selection and multi-class categorization.- On the evaluation of tensor-based representations for optimum-pathforest classification.- On the harmony search using quaternions.- Learning parameters in deep belief networks through firefly algorithm.- Towards effective classification of imbalanced data with convolutional neural networks.- On CPU performance optimization of restricted Boltzmann machine and convolutional RBM.- Comparing incremental learning strategies for convolutional neural networks.- Approximation of graph edit distance by means of a utility matrix.- Time series classification in reservoir- and model-space: a comparison.- Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks.- Background categorization for automatic animal detection in aerial videos using neural networks.- Predictive segmentation using multichannel neural networks in Arabic OCR system.- Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters.- A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins.- Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals.- Active learning for speech event detection in HCI.- Emotion recognition in speech with deep learning architectures.- On gestures and postural behavior as a modality in ensemble methods.- Machine learning driven heart rate detection with camera photoplethysmography in time domain. • ISBN:9783319461816 • Format:Paperback • Publication Date:2016-09-09
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This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback. Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them. Pattern Recognition and Neural Networks (Paperback)
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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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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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Building an Ensemble of Classifiers via Randomized Models of Ensemble Members.- Hybrid learning model for syntactic pattern recognition.- Distance Metrics in Clustering and Weighted Scoring Algorithm.- Exploration of Hardware Acceleration Methods for an XNOR Traffic Signs Classifier.- ALEA: An Anonymous Leader Election Algorithm for Synchronous Distributed Systems.- Comparing concepts of quantum and classical neural network models for image classification task.- Can Color Cryptography Be Truly Random?.- Description-based Ranking of Visual Instances: Feasibility Study for Keypoints.- Fuzzy system for lip print identification.- Assessment of correlations between age and textural features of CT images of thoracic vertebrae.- Assessment of correlations between age and textural features of CT images of thoracic vertebrae.- Application of Image Entropy Analysis for the Quality Assessment of Stitched Images. Lecture Notes in Networks and Systems: Progress in Image Processing, Pattern Recognition and Communication Systems: Proceedings of the Conference (Cores, Ip\u0026c, Acs) – June 28-30 2021 (Paperback)
