Katharina Morik's Books

Local Pattern Detection

International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers

By: Katharina Morik,Jean-Francois Boulicaut

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Local Pattern Detection

International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers

By: Katharina Morik,Jean-Francois Boulicaut

This collection of 13 selected papers originates from the International Seminar on Local Pattern Detection, held in Dagstuhl Castle, Germany in April 2004. This state-of-the-art survey on the emerging field Local Pattern Detection addresses four main topics. Three papers cover frequent set mining, four cover subgroup discovery, three cover the statistical view, and three papers are devoted to time phenomena.

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242

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Springer Science & Business Media

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ISBN 10

3540265430

ISBN 13

9783540265436

GWAI-87 11th German Workshop on Artificial Intelligence

Geseke, September 28–October 2, 1987 Proceedings

By: Katharina Morik

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GWAI-87 11th German Workshop on Artificial Intelligence

Geseke, September 28–October 2, 1987 Proceedings

By: Katharina Morik

Es wurde gezeigt, daß das Bootstrap-Problem bei der geometrischen Szenenrekonstruktion eine wichtige Rolle spielt und daß das Problem der physikalischen Korrespondenz als eine spezielle Sichtweise des Rekonstruktionsproblems gesehen werden kann. Weiterhin wurde ein wissensbasier ter Ansatz vorgestellt, um das Bootstrap-Problem zu umgehen. Literatur Bajcsy + Lieberman 76 : Texture Gradient as a Depth Cue, R. Bajcsy und L.1. Lieberman, Computer Graphics and Image Processing 5, 52-67 (1976). Bartsch u.a. 86 : Merkmalsdetektion in Farbbildern als Grundlage zur Korrespondenzanalyse in Stereo-Bildfolgen, Thomas Bartsch, Leonie S. Dreschler-Fischer und Carsten Schröder, DAGM-86, pp. 94-97. Binford 81 : Inferring Surfaces /rom Images, Thomas O. Binford, Artificial Intelligence 17, 205-244 (1981) siehe auch: Brady 81, pp. 75-116. Blostein + Ahuja 87 : Representation and Three-Dimensional Interpretation of Image Texture: An Integrated Approach, Dorothea Blostein und Narendra Ahuja, ICCV-87, pp. 444-449. Brady 81 : Computer Vision, J.M. Brady (Rrsg.), North Holland Publ. Comp. Amsterdam 1981, reprinted /rom Artificial Intelligence 17 (1981). Clocksin 78 : Determining the Orientation of Surfaces /rom Optical Flow, W.F. Clocksin, Proc. AISB/GI-78 on Artificial Intelligence, Hamburg, July 18-20, 1978, pp. 73-102. Crowley 84a: A Computational Paradigm for Three Dimensional Scene Analysis, James 1. Crowley, Technical Report CMU-RI-TR-84-11 The Robotics Institute, Carnegie Mel10n University, Pittburgh, PA (April 1984). Dreschler + Nagel 82b : Volumetrie Model and 3D-Trajectory of a Moving Car Derived /rom Monocular TV Frame Sequences of aStreet Scene, L. Dreschler und H.-H. Nagel, Computer Graphics and Image Processing 20, 199--228 (1982).

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428

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Springer

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ISBN 10

3540183884

ISBN 13

9783540183884

Local Pattern Detection

International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers

By: Katharina Morik,Jean-Francois Boulicaut,Arno Siebes

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Local Pattern Detection

International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers

By: Katharina Morik,Jean-Francois Boulicaut,Arno Siebes

Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.

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242

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3540318941

ISBN 13

9783540318941

Book's by Katharina Morik

Local Pattern Detection

Local Pattern Detection

GWAI-87 11th German Workshop on Artificial Intelligence

GWAI-87 11th German Workshop on Artificial Intelligence

Local Pattern Detection

Local Pattern Detection

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