Kernels For Structured Data
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| 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.
| Author | : Thomas Grtner |
| 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.
| Author | : John Shawe-Taylor |
| Publisher | : Cambridge University Press |
| Total Pages | : 520 |
| Release | : 2004-06-28 |
| Genre | : Computers |
| ISBN | : 9780521813976 |
| 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.
| Author | : Karsten Borgwardt |
| Publisher | : |
| Total Pages | : 198 |
| Release | : 2020-12-22 |
| Genre | : |
| ISBN | : 9781680837704 |
| 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.
| 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.
| 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.
| 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.
| Author | : Jean-Francois Boulicaut |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 597 |
| Release | : 2004-09-07 |
| Genre | : Computers |
| ISBN | : 3540231056 |
This book constitutes the refereed proceedings of the 15th European Conference on Machine Learning, ECML 2004, held in Pisa, Italy, in September 2004, jointly with PKDD 2004. The 45 revised full papers and 6 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 280 papers submitted to ECML and 107 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
