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

出版时间：2005-8-22  出版社：Springer  作者：Petra Perner; Atsushi Imiya  页数：695  
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内容概要

　　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.

书籍目录

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