Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence

Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence

Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence
Publisher: CRC | ISBN: 0849322782 | edition 1999 | PDF | 367 pages | 15,71 mb

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition.In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.

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Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII

Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII

Research and Development in Intelligent Systems XXVI:
Incorporating Applications and Innovations in Intelligent Systems XVII
504 pages | Springer; 1 edition (December 4, 2009) | 1848829825 | PDF | 14 Mb

The papers in this volume are the refereed papers presented at AI-2009, the Twenty-ninth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2009 in both the technical and the application streams.

They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Reasoning, Data Mining and Machine Learning, Optimisation and Planning, and Knowledge Acquisition and Evolutionary Computation, followed by application stream sections on AI and Design, Commercial Applications of AI and Further AI Applications. The volume also includes the text of short papers presented as posters at the conference.

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Deterministic Learning Theory for Identification, Recognition, and Control

Deterministic Learning Theory for Identification, Recognition, and Control

Deterministic Learning Theory for Identification, Recognition, and Control
207 pages | CRC; 1 edition (July 21, 2009) | 0849375533 | PDF | 12 Mb

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

A Deterministic View of Learning in Dynamic Environments
The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

A New Model of Information Processing
This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

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Introduction to Statistical Pattern Recognition, Second Edition

Introduction to Statistical Pattern Recognition, Second Edition

Introduction to Statistical Pattern Recognition, Second Edition
Publisher: Academic Press | ISBN: 0122698517 | edition 1990 | PDF | 616 pages | 12,5 mb

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

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Pattern Classification

Pattern Classification (Repost)

Pattern Classification
Publisher: Wiley-Interscience | ISBN: 0471056693 | edition 2000 | PDF | 738 pages | 11,28 mb

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.

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Pattern Recognition: Concepts, Methods and Applications (Repost)

Pattern Recognition: Concepts, Methods and Applications (Repost)

Pattern Recognition: Concepts, Methods and Applications
Publisher: Springer | ISBN: 3540422978 | edition 2001 | PDF | 328 pages | 19,94 mb

The book provides a comprehensive view of Pattern Recognition concepts and methods, illustrated with real-life applications in several areas. It is appropriate as a textbook of Pattern Recognition courses and also for professionals and researchers who need to apply Pattern Recognition techniques. These are explained in a unified an innovative way, with multiple examples enhacing the clarification of concepts and the application of methods. Recent methods and results in Pattern Recognition are also presented in a clear way. A CD-ROM offered with the book includes datasets and software tools, making it easier for the reader to follow the taught matters in a hands-on fashion right from the start.

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Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings (Lecture Notes in Compute

Text, Speech and Dialogue

Posted By : holdemorg | Date : 16 Aug 2009 18:08:02 | Comments : 0

Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen, Czech Republic, September 3-7, 2007, Proceedings (Lecture Notes in Computer Science)
Praeger | 663 pages | 2007 | ISBN: 3540746277 | PDF | 9,2 mb

This book constitutes the refereed proceedings of the 10th International Conference on Text, Speech and Dialogue, TSD 2007, held in Pilsen, Czech Republic, September 3-7, 2007.

The 80 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 198 submissions. The papers present a wealth of state-of-the-art research results in the field of natural language processing with an emphasis on text, speech, and spoken dialogue ranging from theoretical and methodological issues to applications in various fields and with special focus on corpora, texts and transcription, speech analysis, recognition and synthesis, as well as their intertwining within NL dialogue systems.

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Digital Human Modeling: First International Conference, DHM 2007, Helt as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceeding

Digital Human Modeling

Posted By : holdemorg | Date : 16 Aug 2009 18:16:27 | Comments : 0

Digital Human Modeling: First International Conference, DHM 2007, Helt as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings (Lecture Notes in Computer Science)
Springer | 1068 pages | 2007 | ISBN: 3540733183 | PDF | 29 mb

This book constitutes the refereed proceedings of the First International Conference on Digital Human Modeling, DHM 2007, held in Beijing, China in July 2007 in the framework of the 12th International Conference on Human-Computer Interaction, HCII 2007 with 8 other thematically similar conferences.

The 118 revised papers presented were carefully reviewed and selected from numerous submissions. The papers accepted for presentation thoroughly cover the thematic area of digital human modeling, addressing the following major topics: shape and movement modeling and anthropometry, building and applying virtual humans, medical and rehabilitation applications, as well as industrial and ergonomic applications.

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Hybrid Methods in Pattern Recognition

Hybrid Methods in Pattern Recognition

Hybrid Methods in Pattern Recognition
Publisher: World Scientific Publishing Company | ISBN: 9810248326 | edition 2002 | PDF | 338 pages | 14,19 mb

Collection of articles describing recent progress in this emerging field. Covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, and others.

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Valeria Bertacco, "Scalable Hardware Verification with Symbolic Simulation"

Scalable Hardware Verification with Symbolic Simulation

Valeria Bertacco, "Scalable Hardware Verification with Symbolic Simulation"
Springer | 2005 | ISBN: 0387244115 | 180 pages | PDF | 10,2 MB

Scalable Hardware Verification with Symbolic Simulation presents recent advancements in symbolic simulation-based solutions which radically improve scalability. It overviews current verification techniques, both based on logic simulation and formal verification methods, and unveils the inner workings of symbolic simulation. The core of this book focuses on new techniques that narrow the performance gap between the complexity of digital systems and the limited ability to verify them. In particular, it covers a range of solutions that exploit approximation and parametrization methods, including quasi-symbolic simulation, cycle-based symbolic simulation, and parameterizations based on disjoint-support decompositions. In structuring this book, the author’s hope was to provide interesting reading for a broad range of design automation readers. The first two chapters provide an overview of digital systems design and, in particular, verification. Chapter 3 reviews mainstream symbolic techniques in formal verification, dedicating most of its focus to symbolic simulation. The fourth chapter covers the necessary principles of parametric forms and disjoint-support decompositions. Chapters 5 and 6 focus on recent symbolic simulation techniques, and the final chapter addresses key topics needing further research. Scalable Hardware Verification with Symbolic Simulation is for verification engineers and researchers in the design automation field. Highlights: A discussion of the leading hardware verification techniques, including simulation and formal verification solutions Important concepts related to the underlying models and algorithms employed in the field The latest innovations in the area of symbolic simulation, exploiting techniques such as parametric forms and decomposition properties of Boolean functions Providing insights into possible new developments in the hardware verification

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3-D Model Recognition from Stereoscopic Cues (Artificial Intelligence Series) By John E.W. Mayhew, John P. Frisby

3-D Model Recognition from Stereoscopic Cues

3-D Model Recognition from Stereoscopic Cues (Artificial Intelligence Series) By John E.W. Mayhew, John P. Frisby
Publisher: MIT Press 1991 | 286 Pages | ISBN: 0262132435 | PDF | 61 MB

3D Model Recognition from Stereoscopic Cues provides a rich, integrated account of work done within a large-scale, multisite, Alvey-funded collaborative project in computer vision. It presents a variety of methods for deriving surface descriptions from stereoscopic data and for matching those descriptions to three-dimensional models for the purposes of object recognition, vision verification, autonomous vehicle guidance, and robot workstation guidance. State of the art vision systems are described in sufficient detail to allow researchers to replicate the results.

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