Kernel Methods In Computational Biology
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Kernel Methods In Computational Biology
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Author : Bernhard Sch?lkopf
language : en
Publisher:
Release Date : 2016
Kernel Methods In Computational Biology written by Bernhard Sch?lkopf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Kernel Methods In Computational Biology
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Author : Bernhard Schölkopf
language : en
Publisher: MIT Press
Release Date : 2004
Kernel Methods In Computational Biology written by Bernhard Schölkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
A detailed overview of current research in kernel methods and their application to computational biology.
Kernel Methods In Bioengineering Signal And Image Processing
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Author : Camps-Valls, Gustavo
language : en
Publisher: IGI Global
Release Date : 2006-11-30
Kernel Methods In Bioengineering Signal And Image Processing written by Camps-Valls, Gustavo and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-30 with Technology & Engineering categories.
"This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.
Kernel Based Data Fusion For Machine Learning
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Author : Shi Yu
language : en
Publisher: Springer
Release Date : 2011-03-29
Kernel Based Data Fusion For Machine Learning written by Shi Yu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-29 with Computers categories.
Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.
Kernel Methods For Pattern Analysis
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Author : John Shawe-Taylor
language : en
Publisher: Cambridge University Press
Release Date : 2004-06-28
Kernel Methods For Pattern Analysis written by John Shawe-Taylor and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-28 with Computers categories.
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Learning With Kernels
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Author : Bernhard Scholkopf
language : en
Publisher: MIT Press
Release Date : 2018-06-05
Learning With Kernels written by Bernhard Scholkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-05 with Computers categories.
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.
Kernel Methods In Bioengineering Signal And Image Processing
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Author : Gustavo Camps-Valls
language : en
Publisher: IGI Global
Release Date : 2007-01-01
Kernel Methods In Bioengineering Signal And Image Processing written by Gustavo Camps-Valls and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-01 with Technology & Engineering categories.
"This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.
Advances In Neural Information Processing Systems 19
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Author : Bernhard Schölkopf
language : en
Publisher: MIT Press
Release Date : 2007
Advances In Neural Information Processing Systems 19 written by Bernhard Schölkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Artificial intelligence categories.
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.
Kernel Methods And Machine Learning
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Author : S. Y. Kung
language : en
Publisher: Cambridge University Press
Release Date : 2014-04-17
Kernel Methods And Machine Learning written by S. Y. Kung and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-17 with Computers categories.
Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.
Machine Learning In Bioinformatics
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Author : Yanqing Zhang
language : en
Publisher: John Wiley & Sons
Release Date : 2009-02-23
Machine Learning In Bioinformatics written by Yanqing Zhang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-23 with Computers categories.
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.