By Ari Visa (auth.), Petra Perner (eds.)
This booklet constitutes the refereed lawsuits of the second one foreign Workshop on laptop studying and information Mining in trend reputation, MLDM 2001, held in Leipzig, Germany in July 2001.
The 26 revised complete papers provided including invited papers have been conscientiously reviewed and chosen for inclusion within the lawsuits. The papers are geared up in topical sections on case-based reasoning and associative reminiscence; rule induction and grammars; clustering and conceptual clustering; information mining on indications, pictures, and spatio-temporal information; nonlinear functionality studying and neural web dependent studying; studying for handwriting reputation; statistical and evolutionary studying; and content-based picture retrieval.
Read or Download Machine Learning and Data Mining in Pattern Recognition: Second International Workshop, MLDM 2001 Leipzig, Germany, July 25–27, 2001 Proceedings PDF
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Extra resources for Machine Learning and Data Mining in Pattern Recognition: Second International Workshop, MLDM 2001 Leipzig, Germany, July 25–27, 2001 Proceedings
Smith  distinguishes into 5 different kinds of similarity: • • • • • Overall similarity Similarity Identity Partial similarity and Partial identity. Case-Based Reasoning and Dissimilarity-Based Classification 41 Overall similarity is a global relation that includes all other similarity relations. All colloquial similarity statements are subsumed here. Similarity and identity are relations that consider all properties of objects at once, no single part is left unconsidered. A red ball and a blue ball are similar, a red ball and a red car are dissimilar.
Temporal Abstractions and Case-Based Reasoning for Medical Course Data 27 Since many different continuations are possible for the same previous course, it is necessary to search for similar courses and different projections. Therefore, we divided the search space into nine parts corresponding to the possible continuation directions. Each direction forms an own part of the search space. During the retrieval these parts are searched separately and each part may provide at most one similar case. The at most 9 similar cases of the 9 parts are presented in the order of their computed similarity values.
The closest case with its associated result is selected and presented as output of the system. Recently, Dissimilarity-based Classification has been introduced due to the curse of dimensionality of feature spaces and the problem arising when trying to make image features explicitly. The approach classifies samples based on their dissimilarity value to all training samples. In this paper, we are reviewing the basic properties of these two approaches. We show the similarity of Dissimilarity based Classification to Case-Based Reasoning.
Machine Learning and Data Mining in Pattern Recognition: Second International Workshop, MLDM 2001 Leipzig, Germany, July 25–27, 2001 Proceedings by Ari Visa (auth.), Petra Perner (eds.)