Advances in Feature Selection for Data and Pattern Recognition, Urszula Sta?czyk; Beata Zielosko; Lakhmi C. Jain

Автор: Yongzhen Huang; Tieniu Tan
Название: Feature Coding for Image Representation and Recognition
ISBN: 3662449994 ISBN-13(EAN): 9783662449998
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline.

Автор: Lars Eld?n
Название: Matrix Methods in Data Mining and Pattern Recognition
ISBN: 0898716268 ISBN-13(EAN): 9780898716269
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed include classification of handwritten digits, text mining, text summarization, pagerank computations related to the Google search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.

Автор: Sergios Theodoridis
Название: Pattern Recognition,
ISBN: 1597492728 ISBN-13(EAN): 9781597492720
Издательство: Elsevier Science
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Цена: 14483.00 р.
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Описание: Considers classical and theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. This book provides an self-contained volume encapsulating this spectrum of information.

Автор: Urszula Sta?czyk; Lakhmi C. Jain
Название: Feature Selection for Data and Pattern Recognition
ISBN: 3662456192 ISBN-13(EAN): 9783662456194
Издательство: Springer
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Цена: 20896.00 р.
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Описание: This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.

Автор: Pramod Kumar Pisharady; Prahlad Vadakkepat; Loh Ai
Название: Computational Intelligence in Multi-Feature Visual Pattern Recognition
ISBN: 9811011710 ISBN-13(EAN): 9789811011719
Издательство: Springer
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Цена: 15672.00 р.
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Описание: This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds.

Автор: Pramod Kumar Pisharady; Prahlad Vadakkepat; Loh Ai
Название: Computational Intelligence in Multi-Feature Visual Pattern Recognition
ISBN: 9812870555 ISBN-13(EAN): 9789812870551
Издательство: Springer
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Цена: 18284.00 р.
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Описание: This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds.

Автор: Ver?nica Bol?n-Canedo; Noelia S?nchez-Maro?o; Ampa
Название: Feature Selection for High-Dimensional Data
ISBN: 3319218573 ISBN-13(EAN): 9783319218571
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Introduction to High-Dimensionality.- Foundations of Feature Selection.- Experimental Framework.- Critical Review of Feature Selection Methods.- Application of Feature Selection to Real Problems.- Emerging Challenges.

Автор: Anthony Mihirana De Silva; Philip H. W. Leong
Название: Grammar-Based Feature Generation for Time-Series Prediction
ISBN: 9812874100 ISBN-13(EAN): 9789812874108
Издательство: Springer
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Цена: 8489.00 р.
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Описание: This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself.

Автор: Adnan Amin; Dov Dori; Pavel Pudil; Herbert Freeman
Название: Advances in Pattern Recognition
ISBN: 3540648585 ISBN-13(EAN): 9783540648581
Издательство: Springer
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Цена: 19564.00 р.
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Описание: "Advances in Pattern Recognition".

Автор: Petra Perner; Patrick Wang; Azriel Rosenfeld
Название: Advances in Structural and Syntactical Pattern Recognition
ISBN: 3540615776 ISBN-13(EAN): 9783540615774
Издательство: Springer
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Цена: 12157.00 р.
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Описание: The papers contained in this text are divided into sections on: grammars and languages; morphology and mathematical approaches to pattern recognition; semantic nets, relational models and graph-bassed methods; 2D and 3D shape recognition; and handwritten and printed character recognition.

Автор: Urszula Sta?czyk; Lakhmi C. Jain
Название: Feature Selection for Data and Pattern Recognition
ISBN: 3662508451 ISBN-13(EAN): 9783662508459
Издательство: Springer
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Цена: 18284.00 р.
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Описание: This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.

Автор: Isabelle Guyon; Steve Gunn; Masoud Nikravesh; Loft
Название: Feature Extraction
ISBN: 366251771X ISBN-13(EAN): 9783662517710
Издательство: Springer
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Цена: 41787.00 р.
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Описание: This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.