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Author: Johan A. K. Suykens Publisher: World Scientific ISBN: 9789812381514 Category : Mathematics Languages : en Pages : 318
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: Johan A. K. Suykens Publisher: World Scientific ISBN: 9789812381514 Category : Mathematics Languages : en Pages : 318
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
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
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
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
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: Jeffrey Racine Publisher: Oxford University Press ISBN: 0199857946 Category : Business & Economics Languages : en Pages : 562
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: Yizeng Liang Publisher: CRC Press ISBN: 1439821283 Category : Computers Languages : en Pages : 206
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
Author: Nikil R. Pal Publisher: Springer Science & Business Media ISBN: 3540239316 Category : Computers Languages : en Pages : 1397
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: Lutz H. Hamel Publisher: John Wiley & Sons ISBN: 1118211030 Category : Computers Languages : en Pages : 211
Book Description
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
Author: Stephen Boyd Publisher: Cambridge University Press ISBN: 1316518965 Category : Business & Economics Languages : en Pages : 477
Book Description
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Author: Johan A. K. Suykens
Author: Johan A. K. Suykens
Author: Jayadeva
Author: Ingo Steinwart
Author: Shigeo Abe
Author: Johan A. K. Suykens
Author: Jeffrey Racine
Author: Yizeng Liang![Neural information processing [electronic resource] PDF](https://books.google.com/books/content/images/frontcover/5lmLYew5zwMC?fife=w80-h100)
Author: Nikil R. Pal
Author: Lutz H. Hamel
Author: Stephen Boyd