Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, July 5-7, 2003, proceedings

by Petra Perner (Editor), Azriel Rosenfeld (Editor)

Overview

The International Conferenceon Machine Learning and Data Mining (MLDM) is the third meeting in a series of biennial events, which started in 1999, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig. MLDM began as a workshop and is now a conference, and has brought the topic of machine learning and data mining to the attention of the research community. Seventy-five papers were submitted to the conference this year. The program committee worked hard to select the most progressive research in a fair and competent review process which led to the acceptance of 33 papers for presentation at the conference. The 33 papers in these proceedings cover a wide variety of topics related to machine learning and data mining. The two invited talks deal with learning in case-based reasoning and with mining for structural data. The contributed papers can be grouped into nine areas: support vector machines; pattern dis- very; decision trees; clustering; classification and retrieval; case-based reasoning; Bayesian models and methods; association rules; and applications. We would like to express our appreciation to the reviewers for their precise and highly professional work. We are grateful to the German Science Foundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial staff at Springer Verlag, and in particular Alfred Hofmann,who supported the publication of these proceedings in the LNAI series. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.

Product Details

ISBN-13:	9783540405047
Publisher:	Springer Berlin Heidelberg
Publication date:	08/13/2003
Series:	Lecture Notes in Computer Science , #2734
Edition description:	2003
Pages:	444
Product dimensions:	6.10(w) x 9.17(h) x 0.36(d)

Table of Contents

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 Sk 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.