Linear and Nonlinear Subspace Methods for Pattern Classification PDF Download
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Author: Bilal Barkana
Publisher:
ISBN:
Category : Algebras, Linear
Languages : en
Pages : 196
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Book Description
Author: Bilal Barkana
Publisher:
ISBN:
Category : Algebras, Linear
Languages : en
Pages : 196
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Book Description
Author: Yen-Wei Chen
Publisher: Springer
ISBN: 3642548512
Category : Technology & Engineering
Languages : en
Pages : 210
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Book Description
This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.
Author: Erkki Oja
Publisher: John Wiley & Sons
ISBN:
Category : Psychology
Languages : en
Pages : 208
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Book Description
Discusses the fundamentals of subspace methods & the different approaches taken; concentrates on the learning subspace method used for automatic speech recognition & more generally for the classification of spectra.
Author: H. J. Greenberg
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Lior Rokach
Publisher: World Scientific
ISBN: 9814271063
Category : Computers
Languages : en
Pages : 242
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Book Description
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.
Author: H. J. Greenberg
Publisher:
ISBN:
Category :
Languages : en
Pages : 307
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Book Description
Author: Haiping Lu
Publisher: CRC Press
ISBN: 1439857245
Category : Computers
Languages : en
Pages : 298
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Book Description
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today’s most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications. The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB® source code, data, and other materials are available at www.comp.hkbu.edu.hk/~haiping/MSL.html
Author: Anand Rangarajan
Publisher: Springer Science & Business Media
ISBN: 3540302875
Category : Computers
Languages : en
Pages : 671
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Book Description
This book constitutes the refereed proceedings of the 5th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2005, held in St. Augustine, FL, USA in November 2005. The 24 revised full papers and 18 poster papers presented were carefully reviewed and selected from 120 submissions. The papers are organized in topical sections on probabilistic and informational approaches, combinatorial approaches, variational approaches, and other approaches and applications.
Author: Marzena Kryszkiewicz
Publisher: Springer
ISBN: 3319199412
Category : Computers
Languages : en
Pages : 596
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Book Description
This book constitutes the proceedings of the 6th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2015, held in Warsaw, Poland, in June/July 2015. The total of 53 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 90 submissions. They were organized in topical sections named: foundations of machine learning; image processing; image retrieval; image tracking; pattern recognition; data mining techniques for large scale data; fuzzy computing; rough sets; bioinformatics; and applications of artificial intelligence.
Author: Bob McKay
Publisher: Springer Science & Business Media
ISBN: 3540001972
Category : Business & Economics
Languages : en
Pages : 744
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Book Description
This book constitutes the refereed proceedings of the 15th Australian Joint Conference on Artificial Intelligence, AI 2002, held in Canberra, Australia in December 2002. The 62 revised full papers and 12 posters presented were carefully reviewed and selected from 117 submissions. The papers are organized in topical sections on natural language and information retrieval, knowledge representation and reasoning, deduction, learning theory, agents, intelligent systems. Bayesian reasoning and classification, evolutionary algorithms, neural networks, reinforcement learning, constraints and scheduling, neural network applications, satisfiability reasoning, machine learning applications, fuzzy reasoning, and case-based reasoning.
