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.
Author(s): Yves Kodratoff (auth.), Georgios Paliouras, Vangelis Karkaletsis, Constantine D. Spyropoulos (eds.)
Series: Lecture Notes in Computer Science 2049 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2001
Language: English
Pages: 324
Tags: Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Database Management; Business Information Systems; User Interfaces and Human Computer Interaction; Mathematical Logic and Formal Languages
Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts....Pages 1-21
Learning Patterns in Noisy Data: The AQ Approach....Pages 22-38
Unsupervised Learning of Probabilistic Concept Hierarchies....Pages 39-70
Function Decomposition in Machine Learning....Pages 71-101
How to Upgrade Propositional Learners to First Order Logic: A Case Study....Pages 102-126
Case-Based Reasoning....Pages 127-145
Genetic Algorithms in Machine Learning....Pages 146-168
Pattern Recognition and Neural Networks....Pages 169-195
Model Class Selection and Construction: Beyond the Procrustean Approach to Machine Learning Applications....Pages 196-217
Integrated Architectures for Machine Learning....Pages 218-229
The Computational Support of Scientic Discovery....Pages 230-248
Support Vector Machines: Theory and Applications....Pages 249-257
Pre- and Post-processing in Machine Learning and Data Mining....Pages 258-266
Machine Learning in Human Language Technology....Pages 267-273
Machine Learning for Intelligent Information Access....Pages 274-280
Machine Learning and Intelligent Agents....Pages 281-285
Machine Learning in User Modeling....Pages 286-294
Data Mining in Economics, Finance, and Marketing....Pages 295-299
Machine Learning in Medical Applications....Pages 300-307
Machine Learning Applications to Power Systems....Pages 308-317
Intelligent Techniques for Spatio-Temporal Data Analysis in Environmental Applications....Pages 318-324
