发现科学：DS 2006/会议录（书与在线文件）/Discovery science
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作者： Nada Lavrac著
出 版 社： 湖南文艺出版社
出版时间： 2006-12-1字数：版次： 1页数： 384印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540464914包装： 平装编辑推荐
The LNAI series reports state-of-the-art results in artificial intelligence re-search, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available.
The scope of LNAI spans the whole range of artificial intelligence and intelli- gent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes.
proceedings (published in time for the respective conference)；
post-proceedings (consisting of thoroughly revised final full papers)；
research monographs (which may be based on PhD work).
内容简介
This book constitutes the refereed proceedings of the 9th International Conference on Discovery Science, DS 2006, held in Barcelona, Spain in October 2006, co-located with the 17th International Conference on Algorithmic Learning Theory, ALT 2006.
The 23 revised long papers and the 18 revised regular papers presented together with 5 invited papers were carefully reviewed and selected from 87 submissions. The papers cover all issues in the area of automating scientific discovery or working on tools for supporting the human process of discovery in science. They address theoretical and methodological topics such as intelligent data analysis, knowledge discovery and machine learning, as well as their application to scientific knowledge discovery.
目录
Ⅰ Invited Papers
e-Science and the Semantic Web: A Symbiotic Relationship
Data-Driven Discovery Using Probabilistic Hidden Variable Models
Reinforcement Learning and Apprenticeship Learning for Robotic Control
The Solution of Semi-Infinite Linear Programs Using Boosting-Like Methods
Spectral Norm in Learning Theory: Some Selected Topics
Ⅱ Long Papers
Classification of Changing Regions Based on Temporal Context in Local Spatial Association
Kalman Filters and Adaptive Windows for Learning in Data Streams
Scientific Discovery: A View from the Trenches
Optimal Bayesian 2D-Discretization for Variable Ranking in Regression
Text Data Clustering by Contextual Graphs
Automatic Water Eddy Detection in SST Maps Using Random Ellipse Fitting and Vectorial Fields for Image Segmentation
Mining Approximate Motifs in Time Series
Identifying Historical Period and Ethnic Origin of Documents Using Stylistic Feature Sets
A New Family of String Classifiers Based on Local Relatedness
On Class Visualisation for High Dimensional Data: Exploring Scientific Data Sets .
Mining Sectorial Episodes from Event Sequences
A Voronoi Diagram Approach to Autonomous Clustering
Itemset Support Queries Using Frequent Itemsets and Their Condensed Representations
Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data
Prediction of Domain-Domain Interactions Using Inductive Logic Programming from Multiple Genome Databases
Clustering Pairwise Distances with Missing Data: Maximum Cuts Versus Normalized Cuts
Analysis of Linux Evolution Using Aligned Source Code Segments
……
Ⅲ Regualar Papers
Author Index
