| The LNCS series reports state-of-the-art results in computer science research，development，and education，at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community，with numerous individuals，as well as with prestigious organizations and societies，LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS，including its subseries LNAI，spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes. —proceedings （published in time for the respective conference） —post-proceedings （consisting of thoroughly revised final full papers） —research monographs（which may be based on outstanding PhD work，research projects，technical reports，etc.） |
| 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 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 …… Author Index |
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