Discovery science : 10th international conference, DS 2007, Sendai, Japan, October 1-4, 2007 ; proceedings

Table of Contents

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   Book Overview
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   Chapter 1 Challenge for Info-plosion
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   Chapter 2 Machine Learning in Ecosystem Informatics
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   Chapter 3 Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity
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   Chapter 4 A Theory of Similarity Functions for Learning and Clustering
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   Chapter 5 A Hilbert Space Embedding for Distributions
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   Chapter 6 Time and Space Efficient Discovery of Maximal Geometric Graphs
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   Chapter 7 Iterative Reordering of Rules for Building Ensembles Without Relearning
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   Chapter 8 On Approximating Minimum Infrequent and Maximum Frequent Sets
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    Chapter 9 A Partially Dynamic Clustering Algorithm for Data Insertion and Removal
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    Chapter 10 Positivism Against Constructivism: A Network Game to Learn Epistemology
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    Chapter 11 Learning Locally Weighted C4.4 for Class Probability Estimation
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    Chapter 12 User Preference Modeling from Positive Contents for Personalized Recommendation
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    Chapter 13 Reducing Trials by Thinning-Out in Skill Discovery
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    Chapter 14 A Theoretical Study on Variable Ordering of Zero-Suppressed BDDs for Representing Frequent Itemsets
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    Chapter 15 Fast NML Computation for Naive Bayes Models
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    Chapter 16 Unsupervised Spam Detection Based on String Alienness Measures
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    Chapter 17 A Consequence Finding Approach for Full Clausal Abduction
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    Chapter 18 Literature-Based Discovery by an Enhanced Information Retrieval Model
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    Chapter 19 Discovering Mentorship Information from Author Collaboration Networks
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    Chapter 20 Active Contours as Knowledge Discovery Methods
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    Chapter 21 An Efficient Polynomial Delay Algorithm for Pseudo Frequent Itemset Mining
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    Chapter 22 Discovering Implicit Feedbacks from Search Engine Log Files
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    Chapter 23 Pharmacophore Knowledge Refinement Method in the Chemical Structure Space
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    Chapter 24 An Attempt to Rebuild C. Bernard’s Scientific Steps
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    Chapter 25 Semantic Annotation of Data Tables Using a Domain Ontology
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    Chapter 26 Model Selection and Estimation Via Subjective User Preferences
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    Chapter 27 Detecting Concept Drift Using Statistical Testing
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    Chapter 28 Towards Future Technology Projection: A Method for Extracting Capability Phrases from Documents
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    Chapter 29 Efficient Incremental Mining of Top-K Frequent Closed Itemsets
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    Chapter 30 An Intentional Kernel Function for RNA Classification
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    Chapter 31 Mining Subtrees with Frequent Occurrence of Similar Subtrees
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    Chapter 32 Semantic Based Real-Time Clustering for PubMed Literatures