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Support Vector Machines

Author: Ingo Steinwart
Publisher: Springer Science & Business Media
ISBN: 0387772421
Size: 59.34 MB
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Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.

Klassifikation Von Niederspannungsnetzen Mit Support Vector Machines Erzeugungsanlagen

Author: Sebastian Breker
Publisher: kassel university press GmbH
ISBN: 3737600147
Size: 43.35 MB
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In dieser Arbeit werden verschiedene Fragestellungen zur Klassifikation von Niederspannungs-(NS)-Netzen hinsichtlich ihrer Aufnahmekapazität für dezentrale Erzeugungsanlagen (DEA) adressiert. Vor diesem Hintergrund werden effiziente Ansätze zur Bewertung von NS-Netzen vorgestellt. Die Anwendungsnähe und Einsatztauglichkeit der erarbeiteten Methoden wird konsequent durch praxisnahe Experimente an Daten einer Vielzahl realer NS-Netze unterstrichen. Ein Einsatz in der Praxis ist daher direkt möglich und kann z. B. zur Erhöhung der Planungssicherheit bei der Steuerung von Investitionsmitteln auf der NS-Ebene dienen. Weiterhin wird durch die Methoden eine strukturierte Möglichkeit zur Auswahl von relevanten NS-Netzstrukturen für detaillierte Untersuchungen geschaffen, indem z. B. von jeder Klasse eine bestimmte Anzahl an NS-Netzen für die Untersuchungen gewählt wird. Auf diese Weise kann der Anteil an „schwachen“ und „starken“ Netzen, die untersucht werden sollen, gesteuert werden, um repräsentative Ergebnisse über alle Klassen zu erhalten.

Support Vector Machines Applications

Author: Yunqian Ma
Publisher: Springer Science & Business Media
ISBN: 3319023004
Size: 21.12 MB
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Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Support Vector Machines Theory And Applications

Author: Lipo Wang
Publisher: Springer Science & Business Media
ISBN: 9783540243885
Size: 79.15 MB
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The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.

Deterministic And Statistical Methods In Machine Learning

Author: Joab Winkler
Publisher: Springer Science & Business Media
ISBN: 3540290737
Size: 64.51 MB
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This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.

Informatics Engineering And Information Science Part Iv

Author: Azizah Abd Manaf
Publisher: Springer
ISBN: 3642254837
Size: 66.56 MB
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This 4-Volume-Set, CCIS 0251 - CCIS 0254, constitutes the refereed proceedings of the International Conference on Informatics Engineering and Information Science, ICIEIS 2011, held in Kuala Lumpur, Malaysia, in November 2011. The 210 revised full papers presented together with invited papers in the 4 volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on e-learning, information security, software engineering, image processing, algorithms, artificial intelligence and soft computing, e-commerce, data mining, neural networks, social networks, grid computing, biometric technologies, networks, distributed and parallel computing, wireless networks, information and data management, web applications and software systems, multimedia, ad hoc networks, mobile computing, as well as miscellaneous topics in digital information and communications.

Neural Networks And Statistical Learning

Author: Ke-Lin Du
Publisher: Springer Science & Business Media
ISBN: 1447155718
Size: 63.12 MB
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Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.

A Gentle Introduction To Support Vector Machines In Biomedicine Theory And Methods

Author: Alexander Statnikov
Publisher: World Scientific
ISBN: 9814324388
Size: 55.26 MB
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Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and cases studies (Volume 2).The proposed book follows the approach of ?programmed learning? whereby material is presented in short sections called ?frames?. Each frame consists of a very small amount of information to be learned, a multiple choice quiz, and answers to the quiz. The reader can proceed to the next frame only after verifying the correct answers to the current frame.

The Nature Of Statistical Learning Theory

Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Size: 69.77 MB
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The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Pattern Recognition With Support Vector Machines

Author: Seong-Whan Lee
Publisher: Springer
ISBN: 3540456651
Size: 71.77 MB
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This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002.The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.