Machine Learning and Data Mining in Pattern Recognition

Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings

Häftad, Engelska, 2003

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This book constitutes the refereed proceedings of the Third International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2003, held in Leipzig, Germany, in July 2003. The 33 revised full papers presented together with two invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on decision trees; clustering and its applications; support vector machines; case-based reasoning; classification, retrieval, and feature Learning; discovery of frequent or sequential patterns; Bayesian models and methods; association rule mining; and applications.

Produktinformation

Utgivningsdatum2003-06-25
Mått155 x 233 x 25 mm
Vikt686 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor444
Upplaga2003
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540405047

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Invited Talkes.- Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers.- Graph-Based Tools for Data Mining and Machine Learning.- Decision Trees.- Simplification Methods for Model Trees with Regression and Splitting Nodes.- Learning Multi-label Alternating Decision Trees from Texts and Data.- Khiops: A Discretization Method of Continuous Attributes with Guaranteed Resistance to Noise.- On the Size of a Classification Tree.- Clustering and Its Applications.- A Comparative Analysis of Clustering Algorithms Applied to Load Profiling.- Similarity-Based Clustering of Sequences Using Hidden Markov Models.- Support Vector Machines.- A Fast Parallel Optimization for Training Support Vector Machine.- A ROC-Based Reject Rule for Support Vector Machines.- Case-Based Reasoning.- Remembering Similitude Terms in CBR.- Authoring Cases from Free-Text Maintenance Data.- Classification, Retrieval, and Feature Learning.- Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation.- Simple Mimetic Classifiers.- Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification.- Estimating a Quality of Decision Function by Empirical Risk.- Efficient Locally Linear Embeddings of Imperfect Manifolds.- Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrieval.- A Rule-Based Scheme for Filtering Examples from Majority Class in an Imbalanced Training Set.- Coevolutionary Feature Learning for Object Recognition.- Discovery of Frequently or Sequential Patterns.- Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints.- Discover Motifs in Multi-dimensional Time-Series Using the Principal Component Analysis and the MDL Principle.- Optimizing Financial Portfolios from the Perspective of Mining Temporal Structures of Stock Returns.- Visualizing Sequences of Texts Using Collocational Networks.- Complexity Analysis of Depth First and FP-Growth Implementations of APRIORI.- Bayesian Models and Methods.- GO-SPADE: Mining Sequential Patterns over Datasets with Consecutive Repetitions.- Using Test Plans for Bayesian Modeling.- Using Bayesian Networks to Analyze Medical Data.- A Belief Networks-Based Generative Model for Structured Documents. An Application to the XML Categorization.- Neural Self-Organization Using Graphs.- Association Rules Mining.- Integrating Fuzziness with OLAP Association Rules Mining.- Discovering Association Patterns Based on Mutual Information.- Applications.- Connectionist Probability Estimators in HMM Arabic Speech Recognition Using Fuzzy Logic.- Shape Recovery from an Unorganized Image Sequence.- A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning.- Detecting the Boundary Curve of Planar Random Point Set.- A Machine Learning Model for Information Retrieval with Structured Documents.