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

One of the superb characteristics of Intelligent Data Analysis (IDA) is that it is an interdisciplinary ?eld in which researchers and practitioners from a number of areas are involved in a typical project. This also creates a challenge in which the success of a team depends on the participation of u...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Famili, A. Fazel (Επιμελητής έκδοσης), Kok, Joost N. (Επιμελητής έκδοσης), Peña, José M. (Επιμελητής έκδοσης), Siebes, Arno (Επιμελητής έκδοσης), Feelders, Ad (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
Σειρά:Lecture Notes in Computer Science, 3646
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.