Machine Learning and Its Applications: Advanced Lectures by Georgios PaliourasMachine Learning and Its Applications: Advanced Lectures by Georgios Paliouras

Machine Learning and Its Applications: Advanced Lectures

byGeorgios PaliourasEditorVangelis Karkaletsis, Constantine D. Spyropoulos

Paperback | August 1, 2001

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In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.
This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Title:Machine Learning and Its Applications: Advanced LecturesFormat:PaperbackDimensions:324 pages, 23.5 × 15.5 × 0.02 inPublished:August 1, 2001Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3540424903

ISBN - 13:9783540424901

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Table of Contents

Methods.- Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts.- Learning Patterns in Noisy Data: The AQ Approach.- Unsupervised Learning of Probabilistic Concept Hierarchies.- Function Decomposition in Machine Learning.- How to Upgrade Propositional Learners to First Order Logic: A Case Study.- Case-Based Reasoning.- Genetic Algorithms in Machine Learning.- Pattern Recognition and Neural Networks.- Model Class Selection and Construction: Beyond the Procrustean Approach to Machine Learning Applications.- Integrated Architectures for Machine Learning.- The Computational Support of Scientic Discovery.- Support Vector Machines: Theory and Applications.- Pre- and Post-processing in Machine Learning and Data Mining.- Machine Learning in Human Language Technology.- Machine Learning for Intelligent Information Access.- Machine Learning and Intelligent Agents.- Machine Learning in User Modeling.- Data Mining in Economics, Finance, and Marketing.- Machine Learning in Medical Applications.- Machine Learning Applications to Power Systems.- Intelligent Techniques for Spatio-Temporal Data Analysis in Environmental Applications.