AI 2009 - PDF Preprint Papers

AI 2009 Australian Conference on Artificial Intelligence

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AI
2009
Springer

186views Artificial Intelligence» more AI 2009»

Grid-Enabled Adaptive Metamodeling and Active Learning for Computer Based Design

13 years 11 months ago

Download www.sumo.intec.ugent.be

Many complex, real world phenomena are difﬁcult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...

Dirk Gorissen

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Id	Views	Title	Status

1	186	Grid-Enabled Adaptive Metamodeling and Active Learning for Computer Based Design	claim
2	172	Novice-Friendly Natural Language Generation Template Authoring Environment	claim
3	169	An Empirical Study of Category Skew on Feature Selection for Text Categorization	claim
4	166	-: A Semantic Web Based Knowledge Representation and Context-Driven Morphing Framework	claim
5	159	An Ontology-Based Spatial Clustering Selection System	claim
6	150	Enhancing the Bilingual Concordancer TransSearch with Word-Level Alignment	claim
7	142	Classifying Biomedical Abstracts Using Committees of Classifiers and Collective Ranking Techniques	claim
8	141	STFLS: A Heuristic Method for Static and Transportation Facility Location Allocation in Large Spatial Datasets	claim
9	136	Enumerating Unlabeled and Root Labeled Trees for Causal Model Acquisition	claim
10	126	Opinion Learning without Emotional Words	claim
11	125	A SVM-Based Ensemble Approach to Multi-Document Summarization	claim
12	124	An Iterative Hybrid Filter-Wrapper Approach to Feature Selection for Document Clustering	claim
13	120	Training Global Linear Models for Chinese Word Segmentation	claim
14	119	Automatic Frame Extraction from Sentences	claim
15	118	Machine Translation of Legal Information and Its Evaluation	claim
16	117	Cost-Based Sampling of Individual Instances	claim
17	113	Executable Specifications of Fully General Attribute Grammars with Ambiguity and Left-Recursion	claim
18	110	Financial Forecasting Using Character N-Gram Analysis and Readability Scores of Annual Reports	claim
19	105	Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model	claim
20	97	Exploratory Analysis of Co-Change Graphs for Code Refactoring	claim
21	91	Modeling and Inference with Relational Dynamic Bayesian Networks	claim

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