Learning with Kernels PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Learning with Kernels PDF full book. Access full book title Learning with Kernels by Bernhard Scholkopf. Download full books in PDF and EPUB format.
Author: Bernhard Scholkopf
Publisher: MIT Press
ISBN: 0262536579
Category : Computers
Languages : en
Pages : 645
Get Book Here
Book Description
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Author: Bernhard Schölkopf
Publisher: MIT Press
ISBN: 9780262194754
Category : Computers
Languages : en
Pages : 658
Get Book Here
Book Description
A comprehensive introduction to Support Vector Machines and related kernel methods.
Author: Johan A.K. Suykens
Publisher: CRC Press
ISBN: 1482241390
Category : Computers
Languages : en
Pages : 528
Get Book Here
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: Bernhard Schölkopf
Publisher: MIT Press
ISBN: 9780262195096
Category : Computers
Languages : en
Pages : 428
Get Book Here
Book Description
A detailed overview of current research in kernel methods and their application to computational biology.
Author: John Shawe-Taylor
Publisher: Cambridge University Press
ISBN: 9780521813976
Category : Computers
Languages : en
Pages : 520
Get Book Here
Book Description
Publisher Description
Author: William W. Hsieh
Publisher: Cambridge University Press
ISBN: 0521791928
Category : Computers
Languages : en
Pages : 364
Get Book Here
Book Description
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Author: Colin Campbell
Publisher: Morgan & Claypool Publishers
ISBN: 1608456161
Category : Computers
Languages : en
Pages : 97
Get Book Here
Book Description
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels
Author: Bernhard Schölkopf
Publisher: Springer
ISBN: 3540451676
Category : Computers
Languages : en
Pages : 761
Get Book Here
Book Description
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Author: Shahar Mendelson
Publisher: Springer
ISBN: 354036434X
Category : Computers
Languages : en
Pages : 267
Get Book Here
Book Description
Machine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11–22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics such as Boosting, Data Mining, Kernel Methods, Logic, Reinforcement Learning, and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, a recorded video of the presentations during the Summer School can be obtained at http://mlg. anu. edu. au/summer2002 It is our hope that graduate students, lecturers, and researchers alike will ?nd this book useful in learning and teaching Machine Learning, thereby continuing the mission of the Summer School. Canberra, November 2002 Shahar Mendelson Alexander Smola Research School of Information Sciences and Engineering, The Australian National University Thanks and Acknowledgments We gratefully thank all the individuals and organizations responsible for the success of the workshop.
Author: Carl Edward Rasmussen
Publisher: Springer Science & Business Media
ISBN: 3540229450
Category : Computers
Languages : en
Pages : 596
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 26th Symposium of the German Association for Pattern Recognition, DAGM 2004, held in Tübingen, Germany in August/September 2004. The 22 revised papers and 48 revised poster papers presented were carefully reviewed and selected from 146 submissions. The papers are organized in topical sections on learning, Bayesian approaches, vision and faces, vision and motion, biologically motivated approaches, segmentation, object recognition, and object recognition and synthesis.
Author: Carl Edward Rasmussen
Publisher: MIT Press
ISBN: 026218253X
Category : Computers
Languages : en
Pages : 266
Get Book Here
Book Description
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
