Discovery Science Third International Conference, DS 2000 Kyoto, Japan, December 4-6, 2000 Proceedings /

This volume contains 3 invited papers, 15 regular papers, and 22 poster papers that were selected for presentation at the Third International Conference on Discovery Science (DS 2000), which was held 4-6 December 2000 in Kyoto. The Program Committee selected the contributed papers from 48 submission...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Arikawa, Setsuo (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Morishita, Shinichi (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.
Έκδοση:1st ed. 2000.
Σειρά:Lecture Notes in Artificial Intelligence ; 1967
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Invited 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.