Machine learning and its applications

Machine Learning and Its Applications: Advanced Lectures<br />Author: Georgios Paliouras, Vangelis Karkaletsis, Constantine D. Spyropoulos<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-42490-1<br /> DOI: 10.1007/3-540-44673-7<br /><br />Table of Contents:<p></p><ul><li>Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts </li><li>Learning Patterns in Noisy Data: The AQ Approach </li><li>Unsupervised Learning of Probabilistic Concept Hierarchies </li><li>Function Decomposition in Machine Learning </li><li>How to Upgrade Propositional Learners to First Order Logic: A Case Study </li><li>Case-Based Reasoning </li><li>Genetic Algorithms in Machine Learning </li><li>Pattern Recognition and Neural Networks </li><li>Model Class Selection and Construction: Beyond the Procrustean Approach to Machine Learning Applications </li><li>Integrated Architectures for Machine Learning </li><li>The Computational Support of Scientic Discovery </li><li>Support Vector Machines: Theory and Applications </li><li>Pre- and Post-processing in Machine Learning and Data Mining </li><li>Machine Learning in Human Language Technology </li><li>Machine Learning for Intelligent Information Access </li><li>Machine Learning and Intelligent Agents </li><li>Machine Learning in User Modeling </li><li>Data Mining in Economics, Finance, and Marketing </li><li>Machine Learning in Medical Applications </li><li>Machine Learning Applications to Power Systems</li></ul>