Least Squares Support Vector Machines PDF Download

Author: Johan A. K. Suykens
Publisher: World Scientific
ISBN: 9789812381514
Category : Mathematics
Languages : en
Pages : 318

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Book Description
This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples.

Author: Jayadeva
Publisher: Springer
ISBN: 3319461869
Category : Technology & Engineering
Languages : en
Pages : 221

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Book Description
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

Author: Ingo Steinwart
Publisher: Springer Science & Business Media
ISBN: 0387772421
Category : Computers
Languages : en
Pages : 611

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Book Description
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.

Author: Shigeo Abe
Publisher: Springer Science & Business Media
ISBN: 1447102851
Category : Computers
Languages : en
Pages : 332

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Book Description
This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.

Author: Johan A. K. Suykens
Publisher: Springer Science & Business Media
ISBN: 9780792381952
Category : Language Arts & Disciplines
Languages : en
Pages : 284

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Book Description
This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.

Author: Nikil R. Pal
Publisher: Springer Science & Business Media
ISBN: 3540239316
Category : Computers
Languages : en
Pages : 1397

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Book Description
Annotation This book constitutes the refereed proceedings of the 11th International Conference on Neural Information Processing, ICONIP 2004, held in Calcutta, India in November 2004. The 186 revised papers presented together with 24 invited contributions were carefully reviewed and selected from 470 submissions. The papers are organized in topical sections on computational neuroscience, complex-valued neural networks, self-organizing maps, evolutionary computation, control systems, cognitive science, adaptive intelligent systems, biometrics, brain-like computing, learning algorithms, novel neural architectures, image processing, pattern recognition, neuroinformatics, fuzzy systems, neuro-fuzzy systems, hybrid systems, feature analysis, independent component analysis, ant colony, neural network hardware, robotics, signal processing, support vector machine, time series prediction, and bioinformatics.

Author: Jeffrey Racine
Publisher: Oxford University Press
ISBN: 0199857946
Category : Business & Economics
Languages : en
Pages : 562

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Book Description
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.

Author: Joachim Diederich
Publisher: Springer
ISBN: 3540753907
Category : Technology & Engineering
Languages : en
Pages : 267

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Book Description
Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.

Author: Johan A.K. Suykens
Publisher: CRC Press
ISBN: 1482241390
Category : Computers
Languages : en
Pages : 528

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Book Description
Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Author: Yizeng Liang
Publisher: CRC Press
ISBN: 1439821283
Category : Computers
Languages : en
Pages : 206

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Book Description
Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar wi