John Shawe-Taylor – författare

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.

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

Artificial intelligence (AI) has been much in the news recently, with some commentators expressing concern that AI might eventually replace humans. But many developments in AI are designed to enhance and supplement the performance of humans rather than replace them, and a novel field of study, with new approaches and solutions to the development of AI, has arisen to focus on this aspect of the technology.This book presents the proceedings of HHAI2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, held from 26-30 June 2023, in Munich, Germany. The HHAI international conference series is focused on the study of artificially intelligent systems that cooperate synergistically, proactively, responsibly and purposefully with humans, amplifying rather than replacing human intelligence, and invites contributions from various fields, including AI, human-computer interaction, the cognitive and social sciences, computer science, philosophy, among others. A total of 78 submissions were received for the main conference track, and most papers were reviewed by at least three reviewers. The overall final acceptance rate was 43%, with 14 contributions accepted as full papers, 14 as working papers, and 6 as extended abstracts. The papers presented here cover topics including interactive hybrid agents; hybrid intelligence for decision support; hybrid intelligence for health; and values such as fairness and trust in hybrid intelligence. We further accepted 17 posters and 4 demos as well as 8 students to the first HHAI doctoral consortium this year. The authors of 4 working papers and 2 doctoral consortium submissions opted for not publishing their submissions to allow a later full submission, resulting in a total of 57 papers included in this proceedings Addressing all aspects of AI systems that assist humans and emphasizing the need for adaptive, collaborative, responsible, interactive, and human-centered artificial intelligence systems which can leverage human strengths and compensate for human weaknesses while considering social, ethical, and legal considerations, the book will be of interest to all those working in the field.

This volume contains papers presented at the 17th Annual Conference on Le- ning Theory (previously known as the Conference on Computational Learning Theory) held in Ban?, Canada from July 1 to 4, 2004. The technical program contained 43 papers selected from 107 submissions, 3 open problems selected from among 6 contributed, and 3 invited lectures. The invited lectures were given by Michael Kearns on 'Game Theory, Automated Trading and Social Networks', Moses Charikar on 'Algorithmic Aspects of - nite Metric Spaces', and Stephen Boyd on 'Convex Optimization, Semide?nite Programming, and Recent Applications'. These papers were not included in this volume. The Mark Fulk Award is presented annually for the best paper co-authored by a student. Thisyear theMark Fulk award wassupplemented with two further awards funded by the Machine Learning Journal and the National Information Communication Technology Centre, Australia (NICTA). We were therefore able toselectthreestudentpapersforprizes.ThestudentsselectedwereMagalieF- montforthesingle-authorpaper"ModelSelectionbyBootstrapPenalizationfor Classi?cation", Daniel Reidenbach for the single-author paper "On the Lear- bility of E-Pattern Languages over Small Alphabets", and Ran Gilad-Bachrach for the paper "Bayes and Tukey Meet at the Center Point" (co-authored with Amir Navot and Naftali Tishby).

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers presented together with 5 invited papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, among others.

Weareproudtopresenttheproceedingsoftheseventhbiennialconferenceinthe Intelligent Data Analysis series. The conference took place in Ljubljana, Slo- nia, September 6-8, 2007. IDA continues to expand its scope, quality and size. It started as a small side-symposium as part of a larger conference in 1995 in Baden-Baden(Germany).It quickly attractedmoreinterest in both submissions and attendance as it moved to London (1997) and then Amsterdam (1999). The next three meetings were held in Lisbon (2001), Berlin (2003) and then Madrid in 2005. The improving quality of the submissions has enabled the organizers to assemble programs of ever-increasing consistency and quality. This year we madea rigorousselectionof33papersoutofalmost100submissions.Theresu- ing oral presentations were then scheduled in a single-track, two-and-a-half-day conference program, summarized in the book that you have before you. In accordance with the stated IDA goal of “bringing together researchers from diverse disciplines,” we believe we have achieved an excellent balance of presentationsfromthemoretheoretical–bothstatisticalandmachinelearning– to the more application-oriented areas that illustrate how these techniques can beusedinpractice.Forexample,theproceedingsincludepaperswiththeoretical contributions dealing with statistical approaches to sequence alignment as well as papers addressing practical problems in the areas of text classi?cation and medical data analysis. It is reassuring to see that IDA continues to bring such diverse areas together, thus helping to cross-fertilize these ?elds.

Weareproudtopresenttheproceedingsoftheseventhbiennialconferenceinthe Intelligent Data Analysis series. The conference took place in Ljubljana, Slo- nia, September 6-8, 2007. IDA continues to expand its scope, quality and size. It started as a small side-symposium as part of a larger conference in 1995 in Baden-Baden(Germany).It quickly attractedmoreinterest in both submissions and attendance as it moved to London (1997) and then Amsterdam (1999). The next three meetings were held in Lisbon (2001), Berlin (2003) and then Madrid in 2005. The improving quality of the submissions has enabled the organizers to assemble programs of ever-increasing consistency and quality. This year we madea rigorousselectionof33papersoutofalmost100submissions.Theresu- ing oral presentations were then scheduled in a single-track, two-and-a-half-day conference program, summarized in the book that you have before you. In accordance with the stated IDA goal of “bringing together researchers from diverse disciplines,” we believe we have achieved an excellent balance of presentationsfromthemoretheoretical–bothstatisticalandmachinelearning– to the more application-oriented areas that illustrate how these techniques can beusedinpractice.Forexample,theproceedingsincludepaperswiththeoretical contributions dealing with statistical approaches to sequence alignment as well as papers addressing practical problems in the areas of text classi?cation and medical data analysis. It is reassuring to see that IDA continues to bring such diverse areas together, thus helping to cross-fertilize these ?elds.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.