Discovery Science: Third International Conference, DS 2000 Kyoto, Japan, December 4-6, 2000 Proceedings / Edition 1by Setsuo Arikawa
Pub. Date: 12/28/2000
Publisher: Springer Berlin Heidelberg
The 15 revised full papers presented together with three invited contributions and 22 posters were carefully reviewed and selected from 48 submissions. Among the topics and areas addressed in their relation to
This book constitutes the refereed proceedings of the Third International Conference on Discovery Science, DS 2000, held in Kyoto, Japan in December 2000.
The 15 revised full papers presented together with three invited contributions and 22 posters were carefully reviewed and selected from 48 submissions. Among the topics and areas addressed in their relation to discovery science are inference, algorithmic learning, heuristic search, database management, data mining, networking, inductive logic programming, information agents, information retrieval, visualization, etc.
- Springer Berlin Heidelberg
- Publication date:
- Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence Series, #1967
- Edition description:
- Product dimensions:
- 6.10(w) x 9.25(h) x 0.36(d)
Table of ContentsInvited Papers.- A Survey of Association-Rule Mining.- Degrees of belief, random worlds, and maximum entropy.- Discovery and Deduction.- Regular Papers.- Integrating Information Visualization and Retrieval for Discovering Internet Sources.- A Unifying Approach to HTML Wrapper Representation and Learning.- Discovery of Web Communities Based on the Co-occurrence of References.- Clustering and Visualization of Large Protein Sequence Databases by Means of an Extension of the Self-Organizing Map.- A Simple Greedy Algorithm for Finding Functional Relations: Efficient Implementation and Average Case Analysis.- Graph-Based Induction for General Graph Structured Data and Its Application to Chemical Compound Data.- Discovering Characteristic Expressions from Literary Works: a New Text Analysis Method beyond N-Gram Statistics and KWIC.- Classifying Scenarios using Belief Decision Trees.- A Practical Algorithm to Find the Best Subsequence Patterns.- On-line Estimation of Hidden Markov Model Parameters.- Computationally Efficient Heuristics for If-Then Rule Extraction from Feed-Forward Neural Networks.- Language Learning with a Neighbor System.- Application of Multivariate Maxwellian Mixture Model to Plasma Velocity Distribution Function.- Knowledge Discovery from fMRI Brain Images by Logical Regression Analysis.- Human Discovery Processes Based on Searching Experiments in Virtual Psychological Research Environment.- Poster Papers.- Prediction of Binding Affinities for Protein-Ligand Complexes with Neural Network Models.- Automatic and Accurate Determination of the Onset Time of the Quasi-periodic Oscillation.- The Role of Choice in Discovery.- Search for New Methods for Assignment of Complex Molecular Spectra.- Computational Analysis for Discovery on the Plasma Waves Observed by Scientific Satellites.- Direction Finding of the Waves in Plasma Using Energy Function.- Coping The Challenge of Mutagenes Discovery with GUHA+/- for Windows.- Discovering Interpretable Rules that Explain Customers’ Brand Choice Behavior.- Mining for 4ft Association Rules.- Rule Discovery Technique Using Genetic Programming Combined with Apriori Algorithm.- Discovery of M-of-N Concepts for Classification.- Issues in Organizing a Successful Knowledge Discovery Contest.- Knowledge Integration of Rule Mining and Schema Discovering.- Discovery of Correlation from Multi-stream of Human Motion.- An Appropriate Abstraction for Constructing a Compact Decision Tree.- Extracting Positive and Negative Keywords for Web Communities.- Nonequilibrium Thermodynamics from Time Series Data Analysis.- Automatic Determination Algorithm for the Optimum Number of States in NL-HMnet.- Comparative Study of Automatic Acquisition Methods of Image Processing Procedures..- Extraction of Authors’ Characteristics from Japanese Modern Sentences via N-gram Distribution.- Combination Retrieval for Creating Knowledge from Sparse Document Collection.- Discovery of Nominally Conditioned Polynomials using Neural Networks, Vector Quantizers and Decision Trees.
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