Titlebook: Machine Learning and Data Mining in Pattern Recognition; Third International Petra Perner,Azriel Rosenfeld Conference proceedings 2003 Spr

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Graph-Based Tools for Data Mining and Machine Learninga mining and machine learning are more involved than their counterparts on feature vectors. However, recent progress in graph matching and related areas has led to many new practical methods that seem to be very promising for a wide range of applications.

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Similarity-Based Clustering of Sequences Using Hidden Markov Modelsis then performed in such a space. By way of this, the difficult problem of clustering of sequences is thus transposed to a more manageable format, the clustering of points (vectors of features). Experimental evaluation on synthetic and real data shows that the proposed approach largely outperforms standard HMM clustering schemes.

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Estimating a Quality of Decision Function by Empirical Riskblem on function space. The problem being very complicate in general case appears to be solvable when the “independent” feature is a space of isolated points. The space has low practical use but it allows scaling well-known estimations by Vapnik and Chervonenkis.

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Dissimilarity Representation of Images for Relevance Feedback in Content-Based Image Retrievalvance scores are described. Reported results on three image databases show that the proposed relevance feedback mechanism allows attaining large improvements in retrieval precision after each retrieval iteration. It also outperforms other techniques proposed in the literature.

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0302-9743 ganized 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

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