Discovery Science

1. Front Matter

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2. Invited Papers

   1. Challenge for Info-plosion

      • Masaru Kitsuregawa

      Pages 1-8

   2. Machine Learning in Ecosystem Informatics

      • Thomas G. Dietterich

      Pages 9-25

   3. Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity

      • Jürgen Schmidhuber

      Pages 26-38

   4. A Theory of Similarity Functions for Learning and Clustering

      • Avrim Blum

      Pages 39-39

   5. A Hilbert Space Embedding for Distributions

      • Alex Smola, Arthur Gretton, Le Song, Bernhard Schölkopf

      Pages 40-41

3. Long Papers

   1. Time and Space Efficient Discovery of Maximal Geometric Graphs

      • Hiroki Arimura, Takeaki Uno, Shinichi Shimozono

      Pages 42-55

   2. Iterative Reordering of Rules for Building Ensembles Without Relearning

      • Paulo J. Azevedo, Alípio M. Jorge

      Pages 56-67

   3. On Approximating Minimum Infrequent and Maximum Frequent Sets

      • Mario Boley

      Pages 68-77

   4. A Partially Dynamic Clustering Algorithm for Data Insertion and Removal

      • Haytham Elghazel, Hamamache Kheddouci, Véronique Deslandres, Alain Dussauchoy

      Pages 78-90

   5. Positivism Against Constructivism: A Network Game to Learn Epistemology

      • Hélène Hagège, Christopher Dartnell, Jean Sallantin

      Pages 91-103

   6. Learning Locally Weighted C4.4 for Class Probability Estimation

      • Liangxiao Jiang, Harry Zhang, Dianhong Wang, Zhihua Cai

      Pages 104-115

   7. User Preference Modeling from Positive Contents for Personalized Recommendation

      • Heung-Nam Kim, Inay Ha, Jin-Guk Jung, Geun-Sik Jo

      Pages 116-126

   8. Reducing Trials by Thinning-Out in Skill Discovery

      • Hayato Kobayashi, Kohei Hatano, Akira Ishino, Ayumi Shinohara

      Pages 127-138

   9. A Theoretical Study on Variable Ordering of Zero-Suppressed BDDs for Representing Frequent Itemsets

      • Shin-ichi Minato

      Pages 139-150

   10. Fast NML Computation for Naive Bayes Models

       • Tommi Mononen, Petri Myllymäki

       Pages 151-160

   11. Unsupervised Spam Detection Based on String Alienness Measures

       • Kazuyuki Narisawa, Hideo Bannai, Kohei Hatano, Masayuki Takeda

       Pages 161-172

   12. A Consequence Finding Approach for Full Clausal Abduction

       • Oliver Ray, Katsumi Inoue

       Pages 173-184

   13. Literature-Based Discovery by an Enhanced Information Retrieval Model

       • Kazuhiro Seki, Javed Mostafa

       Pages 185-196

   14. Discovering Mentorship Information from Author Collaboration Networks

       • V. Suresh, Narayanan Raghupathy, B. Shekar, C. E. Veni Madhavan

       Pages 197-208

This volume contains the papers presented at DS-2007:The Tenth International Conference on Discovery Science held in Sendai, Japan, October 1–4, 2007. The main objective of the Discovery Science (DS) conference series is to p- vide an open forum for intensive discussions and the exchange of new ideas and information among researchers working in the area of automating scienti?c d- covery or working on tools for supporting the human process of discovery in science. It has been a successful arrangement in the past to co-locate the DS conference with the International Conference on Algorithmic Learning Theory (ALT). ThiscombinationofALTandDSallowsforacomprehensivetreatmentof the whole range, from theoretical investigations to practical applications. C- tinuing this tradition, DS 2007 was co-located with the 18th ALT conference (ALT 2007). The proceedings of ALT 2007 were published as a twin volume 4754 of the LNCS series. The International Steering Committee of the Discovery Science conference series provided important advice on a number of issues during the planning of Discovery Science 2007. The members of the Steering Committee are Einoshin Suzuki (Kyushu University, Chair), Achim G.

• Information Retrieval
• algorithms
• classification
• clustering
• data mining
• discovery science
• forecasting
• gene ontology
• image patterns
• inference network
• knowledge discovery
• learning
• learning theory
• machine learning
• science educa

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