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Author: Arikawa, S. (Setsuo), 1941-, Furukawa, Kōichi, International Conference on Discovery Science (2nd : 1999 : Tokyo, Japan)
Added by: sketch
Added Date: 2015-12-30
Language: eng
Subjects: Science, Discoveries in science, Computer science
Publishers: Berlin ; New York Springer
Collections: folkscanomy miscellaneous, folkscanomy, additional collections
ISBN Number: 354066713X
Pages Count: 300
PPI Count: 300
PDF Count: 1
Total Size: 184.19 MB
PDF Size: 10.97 MB
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Edition: [Elektronische Ressource]
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Total Files: 18
Media Type: texts
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Discovery Science: Second International Conference, DS’99 Tokyo, Japan, December 6–8, 1999 Proceedings
Author: Setsuo Arikawa, Koichi Furukawa
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-66713-1
DOI: 10.1007/3-540-46846-3
Table of Contents:
Includes bibliographical references and index
Author: Setsuo Arikawa, Koichi Furukawa
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-66713-1
DOI: 10.1007/3-540-46846-3
Table of Contents:
- The Melting Pot of Automated Discovery: Principles for a New Science
- Expressive Probability Models in Science
- Weighted Majority Decision among Several Region Rules for Scientific Discovery
- CAEP: Classification by Aggregating Emerging Patterns
- An Appropriate Abstraction for an Attribute-Oriented Induction
- Collaborative Hypothesis Testing Processes by Interactive Production Systems
- Computer Aided Discovery of User’s Hidden Interest for Query Restructuring
- Iterative Naive Bayes
- Schema Design for Causal Law Mining from Incomplete Database
- Design and Evaluation of an Environment to Automate the Construction of Inductive Applications
- Designing Views in HypothesisCreator: System for Assisting in Discovery
- Discovering Poetic Allusion in Anthologies of Classical Japanese Poems
- Characteristic Sets of Strings Common to Semi-structured Documents
- Approximation of Optimal Two-Dimensional Association Rules for Categorical Attributes Using Semidefinite Programming
- Data Mining of Generalized Association Rules Using a Method of Partial-Match Retrieval
- Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
- Scheduled Discovery of Exception Rules
- Learning in Constraint Databases
- Discover Risky Active Faults by Indexing an Earthquake Sequence
- Machine Discovery Based on the Co-occurrence of References in a Search Engine
Includes bibliographical references and index
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