Setsuo Arikawa / Koichi Furukawa (eds.)

Discovery Science

Second International Conference, DS'99, Tokyo, Japan, December 6-8, 1999 Proceedings
Herausgegeben:Arikawa, Setsuo; Furukawa, Koichi

• Broschiertes Buch

Produktbeschreibung

This volume contains the papers presented at the Second International Conf- ence on Discovery Science (DS 99), held in Tokyo, Japan, December 6-8, 1999. The conference was colocated with the Tenth International Conference on Al- rithmic Learning Theory (ALT 99). This conference was organized as part of the activities of the Discovery S- ence Project sponsored by Grant-in-Aid for Scienti c Research on Priority Area fromthe MinistryofEducation,Science,SportsandCulture (MESSC)ofJapan. This is a three-year project starting from 1998 that aims to (1) develop new methods for knowledge discovery, (2) install network environments for kno- edge discovery, and (3) establish Discovery Science as a new area of computer science. The aim of this conference is to provide an open forum for intensive disc- sions and interchange of new information among researchers working in the new area of Discovery Science. Topics of interest within the scope of this conference include, but are not limited to, the following areas: Logic for/of knowledge discovery, knowledge d- coverybyinferences,knowledgediscoverybylearningalgorithms,knowledged- coverybyheuristicsearch,scienti cdiscovery,knowledgediscoveryindatabases, data mining, knowledge discovery in network environments, inductive logic p- gramming, abductive reasoning,machine learning,constructive programming as discovery,intelligentnetworkagents,knowledgediscoveryfromunstructuredand multimedia data, statistical methods for knowledge discovery, data and kno- edge visualization, knowledge discovery and human interaction, and human f- tors in knowledge discovery. The DS 99 program committee selected 26 papers and 25 posters/demos from 74 submissions. Papers were selected according to their relevance to the conference, accuracy, signi cance, originality, and presentation quality.

Produktdetails

• Lecture Notes in Computer Science 1721
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 10705393, 978-3-540-66713-1
• 1999.
• Seitenzahl: 392
• Erscheinungstermin: 18. November 1999
• Englisch
• Abmessung: 235mm x 155mm x 22mm
• Gewicht: 510g
• ISBN-13: 9783540667131
• ISBN-10: 354066713X
• Artikelnr.: 09199243

Inhaltsangabe

Invited Papers.- The Melting Pot of Automated Discovery: Principles for a New Science.- Expressive Probability Models in Science.- Contributed Papers.- 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.- Smoothness Prior Approach to Explore the Mean Structure in Large Time Series Data.- Automatic Detection of Geomagnetic Sudden Commencement Using Lifting Wavelet Filters.- A Noise Resistant Model Inference System.- A Graphical Method for Parameter Learning of Symbolic-Statistical Models.- Parallel Execution for Speeding Up Inductive Logic Programming Systems.- Discovery of a Set ofNominally Conditioned Polynomials.- H-Map: A Dimension Reduction Mapping for Approximate Retrieval of Multi-dimensional Data.- Normal Form Transformation for Object Recognition Based on Support Vector Machines.- Posters.- A Definition of Discovery in Terms of Generalized Descriptional Complexity.- Feature Selection Using Consistency Measure.- A Model of Children's Vocabulary Acquisition Using Inductive Logic Programming.- Automatic Acquisition of Image Processing Procedures from Sample Sets of Classified Images Based on Requirement of Misclassification Rate.- "Thermodynamics" from Time Series Data Analysis.- Developing a Knowledge Network of URLs.- Derivation of the Topology Structure from Massive Graph Data.- Mining Association Algorithm Based on ROC Convex Hull Method in Bibliographic Navigation System.- Regularization of Linear Regression Models in Various Metric Spaces.- Argument-Based Agent Systems.- Graph-Based Induction for General Graph Structured Data.- Rules Extraction by Constructive Learning of Neural Networks and Hidden-Unit Clustering.- Weighted Majority Decision among Region Rules for a Categorical Dataset.- Rule Discovery Technique Using GP with Crossover to Maintain Variety.- From Visualization to Interactive Animation of Database Records.- Extraction of Primitive Motion for Human Motion Recognition.- Finding Meaningful Regions Containing Given Keywords from Large Text Collections.- Mining Adaptation Rules from Cases in CBR Systems.- An Automatic Acquisition of Acoustical Units for Speech Recognition Based on Hidden Markov Network.- Knowledge Discovery from Health Data Using Weighted Aggregation Classifiers.- Search for New Methods for Assignment of Complex Molecular Spectra.- Automatic Discovery of Definition Patterns Based on the MDLPrinciple.- Detection of the Structure of Particle Velocity Distribution by Finite Mixture Distribution Model.- Mutagenes Discovery Using PC GUHA Software System.- Discovering the Primary Factors of Cancer from Health and Living Habit Questionnaires.