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Produktbild: Advances in Intelligent Data Analysis VI
Band 3646

Advances in Intelligent Data Analysis VI 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005, Proceedings

Aus der Reihe Lecture Notes in Computer Science

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.08.2005

Abbildungen

XIV, 534 p.

Herausgeber

A. Fazel Famili + weitere

Verlag

Springer Berlin

Seitenzahl

534

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

809 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-540-28795-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.08.2005

Abbildungen

XIV, 534 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

534

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

809 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-540-28795-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Advances in Intelligent Data Analysis VI
  • Probabilistic Latent Clustering of Device Usage.- Condensed Nearest Neighbor Data Domain Description.- Balancing Strategies and Class Overlapping.- Modeling Conditional Distributions of Continuous Variables in Bayesian Networks.- Kernel K-Means for Categorical Data.- Using Genetic Algorithms to Improve Accuracy of Economical Indexes Prediction.- A Distance-Based Method for Preference Information Retrieval in Paired Comparisons.- Knowledge Discovery in the Identification of Differentially Expressed Genes in Tumoricidal Macrophage.- Searching for Meaningful Feature Interactions with Backward-Chaining Rule Induction.- Exploring Hierarchical Rule Systems in Parallel Coordinates.- Bayesian Networks Learning for Gene Expression Datasets.- Pulse: Mining Customer Opinions from Free Text.- Keystroke Analysis of Different Languages: A Case Study.- Combining Bayesian Networks with Higher-Order Data Representations.- Removing Statistical Biases in Unsupervised Sequence Learning.- Learning from Ambiguously Labeled Examples.- Learning Label Preferences: Ranking Error Versus Position Error.- FCLib: A Library for Building Data Analysis and Data Discovery Tools.- A Knowledge-Based Model for Analyzing GSM Network Performance.- Sentiment Classification Using Information Extraction Technique.- Extending the SOM Algorithm to Visualize Word Relationships.- Towards Automatic and Optimal Filtering Levels for Feature Selection in Text Categorization.- Block Clustering of Contingency Table and Mixture Model.- Adaptive Classifier Combination for Visual Information Processing Using Data Context-Awareness.- Self-poised Ensemble Learning.- Discriminative Remote Homology Detection Using Maximal Unique Sequence Matches.- From Local Pattern Mining to Relevant Bi-cluster Characterization.- Machine-Learning with Cellular Automata.- MDS polar : A New Approach for Dimension Reduction to Visualize High Dimensional Data.- Miner Ants Colony: A New Approach to Solve a Mine Planning Problem.- Extending the GA-EDA Hybrid Algorithm to Study Diversification and Intensification in GAs and EDAs.- Spatial Approach to Pose Variations in Face Verification.- Analysis of Feature Rankings for Classification.- A Mixture Model-Based On-line CEM Algorithm.- Reliable Hierarchical Clustering with the Self-organizing Map.- Statistical Recognition of Noun Phrases in Unrestricted Text.- Successive Restrictions Algorithm in Bayesian Networks.- Modelling the Relationship Between Streamflow and Electrical Conductivity in Hollin Creek, Southeastern Australia.- Biological Cluster Validity Indices Based on the Gene Ontology.- An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering.- Dealing with Data Corruption in Remote Sensing.- Regularized Least-Squares for Parse Ranking.- Bayesian Network Classifiers for Time-Series Microarray Data.- Feature Discovery in Classification Problems.- A New Hybrid NM Method and Particle Swarm Algorithm for Multimodal Function Optimization.- Detecting Groups of Anomalously Similar Objects in Large Data Sets.