Buch, Englisch, Band 3646, 534 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 809 g
6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005, Proceedings
Buch, Englisch, Band 3646, 534 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 809 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-540-28795-7
Verlag: Springer Berlin Heidelberg
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
- Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware SAP
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
Weitere Infos & Material
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.