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Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists
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Human Face Recognition Using Third-Order Synthetic Neural Networks (The Springer International Series in Engineering and C…
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NEURAL NETWORK: PATTERN RECOGNITION USING NEURAL NETWORK
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Anomaly Detection Using a Variational Autoencoder Neural Network With a Novel Objective Function and Gaussian Mixture Mode…
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Action Recognition: Step-by-step Recognizing Actions with Python and Recurrent Neural Network (Computer Vision and Machine…
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Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and …
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Pattern Recognition Using Neural and Functional Networks (Studies in Computational Intelligence)
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Neural Networks using C#: From basic perceptrons to fully functional feedforward multilayer perceptrons
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Palm Print Identity Verification Using Hierarchical Neural Network Architecture: A Graduate Research In Information Techno…
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Palm Print Identity Verification Using Hierarchical Neural Network Architecture: A Graduate Research In Information Techno…
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Unsupervised Learning in Space and Time (Advances in Computer Vision and Pattern Recognition)
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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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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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Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems / Edition 1
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Observational calculi were introduced in the 1960’s as a tool of logic of discovery. Formulas of observational calculi correspond to assertions on analysed data. Truthfulness of suitable assertions can lead to acceptance of new scientific hypotheses. The general goal was to automate the process of discovery of scientific knowledge using mathematical logic and statistics. The GUHA method for producing true formulas of observational calculi relevant to the given problem of scientific discovery was developed. Theoretically interesting and practically important results on observational calculi were achieved. Special attention was paid to formulas – couples of Boolean attributes derived fro
m columns of the analysed data matrix. Association rules introduced in the 1990’s can be seen as a special case of such formulas. New results on logical calculi and association rules were achieved. They can be seen as a logic of association rules. This can contribute to solving contemporary challenging problems of data mining research and practice. The book covers thoroughly the logic of association rules and puts it into the context of current research in data mining. Examples of applications of theoretical results to real problems are presented. New open problems and challenges are listed. Overall, the book is a valuable source of information for researchers as well as for teachers and students interested in data mining. • Author: Jan Rauch • ISBN:9783642445330 • Format:Paperback • Publication Date:2015-01-29
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This book provides an introduction to the use of automated methods for gathering competitive strategic intelligence. It examines the gathering of knowledge that appears of paramount importance to organizations. Introduction.- Research Foundations.- Competitive Intelligence Capturing Systems.- Research Topics and Applications.- Conclusion. • Author: Cai-Nicolas Ziegler • ISBN:9783642277139 • Format:Hardcover • Publication Date:2012-03-14
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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)
