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Author: Thomas Grtner
Publisher: World Scientific
ISBN: 9812814558
Category : Computers
Languages : en
Pages : 216
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Book Description
This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.
Author: Thomas Grtner
Publisher: World Scientific
ISBN: 9812814558
Category : Computers
Languages : en
Pages : 216
Get Book Here
Book Description
This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.
Author: John Shawe-Taylor
Publisher: Cambridge University Press
ISBN: 9780521813976
Category : Computers
Languages : en
Pages : 520
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Book Description
Publisher Description
Author: Bernhard Schölkopf
Publisher: MIT Press
ISBN: 9780262195096
Category : Computers
Languages : en
Pages : 428
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Book Description
A detailed overview of current research in kernel methods and their application to computational biology.
Author: Karsten Borgwardt
Publisher:
ISBN: 9781680837704
Category :
Languages : en
Pages : 198
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Book Description
Author: John Shawe-Taylor
Publisher: Cambridge University Press
ISBN: 1139451618
Category : Computers
Languages : en
Pages : 520
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Book Description
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Author: Stan Matwin
Publisher: Springer Science & Business Media
ISBN: 9783540005674
Category : Computers
Languages : en
Pages : 372
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Book Description
This book constitutes the thoroughly refereed post-proceedings of the 12th International Conference on Inductive Logic Programming, ILP 2002, held in Sydney, Australia in July 2002. The 22 revised full papers presented were carefully selected during two rounds of reviewing and revision from 45 submissions. Among the topics addressed are first order decision lists, learning with description logics, bagging in ILP, kernel methods, concept learning, relational learners, description logic programs, Bayesian classifiers, knowledge discovery, data mining, logical sequences, theory learning, stochastic logic programs, machine discovery, and relational pattern discovery.
Author: Karl Aberer
Publisher: Springer Science & Business Media
ISBN: 3540762973
Category : Business & Economics
Languages : en
Pages : 998
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Book Description
This book constitutes the refereed proceedings of the joint 6th International Semantic Web Conference, ISWC 2007, and the 2nd Asian Semantic Web Conference, ASWC 2007, held in Busan, Korea, in November 2007. The 50 revised full academic papers and 12 revised application papers presented together with 5 Semantic Web Challenge papers and 12 selected doctoral consortium articles were carefully reviewed and selected from a total of 257 submitted papers to the academic track and 29 to the applications track. The papers address all current issues in the field of the semantic Web, ranging from theoretical and foundational aspects to various applied topics such as management of semantic Web data, ontologies, semantic Web architecture, social semantic Web, as well as applications of the semantic Web. Short descriptions of the top five winning applications submitted to the Semantic Web Challenge competition conclude the volume.
Author:
Publisher: IOS Press
ISBN:
Category :
Languages : en
Pages : 3525
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Book Description
Author: Stan Matwin
Publisher: Springer Science & Business Media
ISBN: 3540005676
Category : Computers
Languages : en
Pages : 361
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Book Description
This book constitutes the thoroughly refereed post-proceedings of the 12th International Conference on Inductive Logic Programming, ILP 2002, held in Sydney, Australia in July 2002. The 22 revised full papers presented were carefully selected during two rounds of reviewing and revision from 45 submissions. Among the topics addressed are first order decision lists, learning with description logics, bagging in ILP, kernel methods, concept learning, relational learners, description logic programs, Bayesian classifiers, knowledge discovery, data mining, logical sequences, theory learning, stochastic logic programs, machine discovery, and relational pattern discovery.
Author: Michel Neuhaus
Publisher: World Scientific
ISBN: 9812708170
Category : Computers
Languages : en
Pages : 245
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Book Description
In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain ? commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time.
