Advances in Intelligent Data Analysis. Reasoning about Data: Second International Symposium, IDA-97, London, UK, August 4-6, 1997, Proceedings
Xiaohui Liu, Paul Cohen, Michael R. Berthold
Springer, Aug 22, 1997 - Mathematical statistics - 621 pages
This book constitutes the refereed proceedings of the Second International Symposium on Intelligent Data Analysis, IDA-97, held in London, UK, in August 1997. The volume presents 50 revised full papers selected from a total of 107 submissions. Also included is a keynote, Intelligent Data Analysis: Issues and Opportunities, by David J. Hand. The papers are organized in sections on exploratory data analysis, preprocessing and tools; classification and feature selection; medical applications; soft computing; knowledge discovery and data mining; estimation and clustering; data quality; qualitative models.
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Decomposition of Heterogeneous Classification Problems
Managing Dialogue in a Statistical Expert Assistant with a Cluster
How to Find BigOh in Your Data Set and How Not to
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Abstract accuracy Advances in Intelligent applied approach Artificial Intelligence Artificial Neural Networks Berlin Heidelberg 1997 Berthold Eds complex concept constructive induction cross-validation data mining data sets database decision tree Defect defined described diagnosis discovery distribution encoding error rate estimate evaluation example experiments extracted feature vector functional links fuzzy clustering fuzzy sets generalisation genetic algorithms Gibbs Sampling heuristic hidden units hypothesis IEEE implemented increase input Intelligent Data Analysis linear LNCS Ltree Machine Learning measure meta-reasoning methods Mill's methods monitoring neural networks node noise object operation optimal outliers output parameters partition patient pattern performance possible prediction probability procedure prototype pruning query random regression relation representation space rules sample selection Springer-Verlag Berlin Heidelberg statistical strategy string structure subset subtrees Table techniques test set tion tool training data training set transformation tuples types variables