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Machine Learning and Knowledge Discovery in Databases

European Conference, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I

  • Conference proceedings
  • © 2008
  • Latest edition

Overview

Editors:
  1. Walter Daelemans
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  2. Bart Goethals
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  3. Katharina Morik
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5211)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: ECML PKDD 2008.

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About this book

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

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Table of contents (62 papers)

  1. Front Matter

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  2. Invited Talks (Abstracts)

    1. Industrializing Data Mining, Challenges and Perspectives

      • Françoise Fogelman-Soulié
      Pages 1-1

    2. From Microscopy Images to Models of Cellular Processes

      • Yoav Freund
      Pages 2-2

    3. Data Clustering: 50 Years Beyond K-means

      • Anil K. Jain
      Pages 3-4

    4. Learning Language from Its Perceptual Context

      • Raymond J. Mooney
      Pages 5-5

    5. The Role of Hierarchies in Exploratory Data Mining

      • Raghu Ramakrishnan
      Pages 6-6

  3. Machine Learning Journal Abstracts

    1. Rollout Sampling Approximate Policy Iteration

      • Christos Dimitrakakis, Michail G. Lagoudakis
      Pages 7-7

    2. New Closed-Form Bounds on the Partition Function

      • Krishnamurthy Dvijotham, Soumen Chakrabarti, Subhasis Chaudhuri
      Pages 8-8

    3. Large Margin vs. Large Volume in Transductive Learning

      • Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
      Pages 9-10

    4. Incremental Exemplar Learning Schemes for Classification on Embedded Devices

      • Ankur Jain, Daniel Nikovski
      Pages 11-11

    5. A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity

      • Heng Luo, Changyong Niu, Ruimin Shen, Carsten Ullrich
      Pages 12-12

    6. A Critical Analysis of Variants of the AUC

      • Stijn Vanderlooy, Eyke Hüllermeier
      Pages 13-13

    7. Improving Maximum Margin Matrix Factorization

      • Markus Weimer, Alexandros Karatzoglou, Alex Smola
      Pages 14-14

  4. Data Mining and Knowledge Discovery Journal Abstracts

    1. Finding Reliable Subgraphs from Large Probabilistic Graphs

      • Petteri Hintsanen, Hannu Toivonen
      Pages 15-15

    2. A Space Efficient Solution to the Frequent String Mining Problem for Many Databases

      • Adrian Kügel, Enno Ohlebusch
      Pages 16-16

    3. The Boolean Column and Column-Row Matrix Decompositions

      • Pauli Miettinen
      Pages 17-17

    4. SkyGraph: An Algorithm for Important Subgraph Discovery in Relational Graphs

      • Apostolos N. Papadopoulos, Apostolos Lyritsis, Yannis Manolopoulos
      Pages 18-18

    5. Mining Conjunctive Sequential Patterns

      • Chedy Raïssi, Toon Calders, Pascal Poncelet
      Pages 19-19

    6. Adequate Condensed Representations of Patterns

      • Arnaud Soulet, Bruno Crémilleux
      Pages 20-21

    7. Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data

      • Jimeng Sun, Charalampos E. Tsourakakis, Evan Hoke, Christos Faloutsos, Tina Eliassi-Rad
      Pages 22-22
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  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

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