模式识别中的机器学习与数据挖掘/Machine Learning and Data Mining in Pattern Recognition

2005-8-22  

Springer  

Petra Perner; Atsushi Imiya  

695  

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　　This book constitutes the refereed proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005, held in Leipzig, Germany, in July 2005.　　The 68 revised full papers presented were carefully reviewed and selected. The papers are organized in topical sections on classification and model estimation, neural methods, subspace methods, basics and applications of clustering, feature grouping, discretization, selection and transformation, applications in medicine, time series and sequential pattern mining, mining images in computer vision, mining images and texture, mining motion from sequence, speech analysis, aspects of data mining, text mining, and as a special track: industrial applications of data mining.

Classification and Model Estimation On ECOC as Binary Ensemble Classifiers Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis Parameter Inference of Cost-Sensitive Boosting Algorithms Finite Mixture Models with Negative Components MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection Principles of Multi-kernel Data MiningNeural Methods Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks Determining Regularization Parameters for Derivative Free Neural Learning A Comprehensible SOM-Based Scoring SystemSubspace Methods The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets SSC: Statistical Subspace Clustering Understanding Patterns with Different Subspace ClassificationClustering: Basics Using Clustering to Learn Distance Functions for Supervised Similarity Assessment Linear Manifold Clustering Universal Clustering with Regularization in Probabilistic Space Acquisition of Concept Descriptions by Conceptual ClusteringApplications of Clustering Clustering Large Dynamic Datasets Using Exemplar Points Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files Alarm Clustering for Intrusion Detection Systems in Computer Networks Clustering Document Images Using Graph SummariesFeature Grouping, Diseretization, Selection and Transformation Feature Selection Method Using Preferences Aggregation ……Applications in Medicine Time Series and Sequential Pattern Mining Mining Images in Computer VisionMining Images and Texture Mining Motion from Sequence Speech AnalysisAspects of Data Mining Text Mining Special Track:Industrial Applecations of Data Mining Author Index

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