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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

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.

In supervised learning, the learning machine is given a training set of examples (
or inputs) with associated labels (or output values). Usually the examples are in
the form of attribute vectors, so that the input space is a subset of W. Once the
attribute vectors are available, a number of sets of hypotheses could be chosen
for the problem. Among these, linear functions are the best understood and
simplest to apply. Traditional statistics and the classical neural networks literature
have ...

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.

Schaal, S.: Is Imitation Learning the Route to Humanoid Robots? Trends in
Cognitive Sciences 3(6), 233–242 (1999) 2. Abbeel, P., Ng, A.Y.: Apprenticeship
Learning via Inverse Reinforcement Learning. In: Proceedings of the Twenty-first
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