Kernels for Structured Data

Kernels for Structured Data
Author: Thomas Gartner
Publisher: World Scientific
Total Pages: 216
Release: 2008
Genre: Computers
ISBN: 9812814566

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.

Kernels for Structured Data

Kernels for Structured Data
Author: Thomas G„rtner
Publisher: World Scientific
Total Pages: 216
Release: 2008
Genre: Computers
ISBN: 9812814558

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.

Kernel Methods in Computational Biology

Kernel Methods in Computational Biology
Author: Bernhard Schölkopf
Publisher: MIT Press
Total Pages: 428
Release: 2004
Genre: Computers
ISBN: 9780262195096

A detailed overview of current research in kernel methods and their application to computational biology.

Graph Kernels

Graph Kernels
Author: Karsten Borgwardt
Publisher:
Total Pages: 198
Release: 2020-12-22
Genre:
ISBN: 9781680837704

Predicting Structured Data

Predicting Structured Data
Author: Neural Information Processing Systems Foundation
Publisher: MIT Press
Total Pages: 361
Release: 2007
Genre: Algorithms
ISBN: 0262026171

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

Inductive Logic Programming

Inductive Logic Programming
Author: Stan Matwin
Publisher: Springer Science & Business Media
Total Pages: 372
Release: 2003-02-12
Genre: Computers
ISBN: 9783540005674

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.

The Semantic Web

The Semantic Web
Author: Karl Aberer
Publisher: Springer Science & Business Media
Total Pages: 998
Release: 2007-10-22
Genre: Business & Economics
ISBN: 3540762973

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.

Inductive Logic Programming

Inductive Logic Programming
Author: Stan Matwin
Publisher: Springer Science & Business Media
Total Pages: 361
Release: 2003-02-12
Genre: Computers
ISBN: 3540005676

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.

Bridging the Gap Between Graph Edit Distance and Kernel Machines

Bridging the Gap Between Graph Edit Distance and Kernel Machines
Author: Michel Neuhaus
Publisher: World Scientific
Total Pages: 245
Release: 2007
Genre: Computers
ISBN: 9812708170

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.