Machine Learning and Data Mining in Pattern Recognition

1. Front Matter

   Pages I-XIII

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2. Invited Talkes

   1. Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers

      • Susan Craw

      Pages 1-6

   2. Graph-Based Tools for Data Mining and Machine Learning

      • Horst Bunke

      Pages 7-19

3. Decision Trees

   1. Simplification Methods for Model Trees with Regression and Splitting Nodes

      • Michelangelo Ceci, Annalisa Apice, Donato Malerba

      Pages 20-34

   2. Learning Multi-label Alternating Decision Trees from Texts and Data

      • Francesco De Comité, Rémi Gilleron, Marc Tommasi

      Pages 35-49

   3. Khiops: A Discretization Method of Continuous Attributes with Guaranteed Resistance to Noise

      • Marc Boullé

      Pages 50-64

   4. On the Size of a Classification Tree

      • Angela Scaringella

      Pages 65-72

4. Clustering and Its Applications

   1. A Comparative Analysis of Clustering Algorithms Applied to Load Profiling

      • Fátima Rodrigues, Jorge Duarte, Vera Figueiredo, Zita Vale, M. Cordeiro

      Pages 73-85

   2. Similarity-Based Clustering of Sequences Using Hidden Markov Models

      • Manuele Bicego, Vittorio Murino, Mário A. T. Figueiredo

      Pages 86-95

5. Support Vector Machines

   1. A Fast Parallel Optimization for Training Support Vector Machine

      • Jian-xiong Dong, Adam Krzyżak, Ching_Y. Suen

      Pages 96-105

   2. A ROC-Based Reject Rule for Support Vector Machines

      • Francesco Tortorella

      Pages 106-120

6. Case-Based Reasoning

   1. Remembering Similitude Terms in CBR

      • Eva Armengol, Enric Plaza

      Pages 121-130

   2. Authoring Cases from Free-Text Maintenance Data

      • Chunsheng Yang, Robert Orchard, Benoit Farley, Maivin Zaluski

      Pages 131-140

7. Classification, Retrieval, and Feature Learning

   1. Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation

      • Johel Mitéran, Sebastien Bouillant, Elbey Bourennane

      Pages 141-155

   2. Simple Mimetic Classifiers

      • V. Estruch, C. Ferri, J. Hernández-Orallo, M. J. Ramírez-Quintana

      Pages 156-171

   3. Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification

      • Nizar Bouguila, Djemel Ziou, Jean Vaillancourt

      Pages 172-181

   4. Estimating a Quality of Decision Function by Empirical Risk

      • Victor Mikhailovich Nedel’ko

      Pages 182-187

   5. Efficient Locally Linear Embeddings of Imperfect Manifolds

      • Abdenour Hadid, Matti Pietikäinen

      Pages 188-201

   6. Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrieval

      • Giorgio Giacinto, Fabio Roli

      Pages 202-214

   7. A Rule-Based Scheme for Filtering Examples from Majority Class in an Imbalanced Training Set

      • Jamshid Dehmeshki, Mustafa Karaköy, Manlio Valdivieso Casique

      Pages 215-223

TheInternationalConferenceonMachineLearningandDataMining(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-?ve papers were submitted to the conference this year. The program committeeworkedhardtoselectthemostprogressiveresearchinafairandc- petent 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; classi?cation 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 andhighlyprofessionalwork.WearegratefultotheGermanScienceFoundation for its support of the Eastern European researchers. We appreciate the help and understanding of the editorial sta? at Springer Verlag, and in particular Alfred Hofmann,whosupportedthepublicationoftheseproceedingsintheLNAIseries. Last, but not least, we wish to thank all the speakers and participants who contributed to the success of the conference.

• Bayesian network
• Fuzzy
• LA
• Support Vector Machine
• algorithmic learning
• algorithms
• autonom
• classification
• decision trees
• intelligent data analysis
• machine learning
• object recognition
• pattern recognition
• principal component analysis
• statistical learning

• Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

  Petra Perner

• Center for Automation Research, University of Maryland, College Park, USA

  Azriel Rosenfeld

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