Advances in Intelligent Data Analysis VI智能数据分析进展 VI/会议录

2005-9  

1 (2005年9月30日)  

A. Fazel Famili  

522  

This book constitutes the refereed proceedings of the 6th International Conference on Intelligent Data Analysis, IDA 2005, held in Madrid, Spain in September 2005. The 46 revised papers presented together with two tutorials and two invited talks were carefully reviewed and selected from 184 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

Probabilistic Latent Clustering of Device UsageCondensed Nearest Neighbor Data Domain DescriptionBalancing Strategies and Class OverlappingModeling Conditional Distributions of Continuous Variables in Bayesian NetworksKernel K-Means for Categorical DataUsing Genetic Algorithms to Improve Accuracy of Economical Indexes PredictionA Distance-Based Method for Preference Information Retrieval in Paired ComparisonsKnowledge Discovery in the Identification of Differentially Expressed GenesSearching for Meaningful Feature Interactions with Backward-Chaining Rule InductionExploring Hierarchical Rule Systems in Parallel CoordinatesBayesian Networks Learning for Gene Expression DatasetsPulse: Mining Customer Opinions from Free TextKeystroke Analysis of Different Languages: A Case StudyCombining Bayesian Networks with Higher-Order Data RepresentationsRemoving Statistical Biases in Unsupervised Sequence LearningLearning from Ambiguously Labeled ExamplesLearning Label Preferences: Ranking Error Versus Position ErrorFCLib: A Library for Building Data Analysis and Data Discovery ToolsA Knowledge-Based Model for Analyzing GSM Network PerformanceSentiment Classification Using Information Extraction TechniqueExtending the SOM Algorithm to Visualize Word RelationshipsTowards Automatic and Optimal Filtering Levels for Feature Selection in Text CategorizationBlock Clustering of Contingency Table and Mixture ModelAdaptive Classifier Combination for Visual Information Processing Using Data Context-AwarenessSelf-poised Ensemble Learning……Author Index

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