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» Evaluating algorithms that learn from data streams
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ICML
2006
IEEE
16 years 7 months ago
Active sampling for detecting irrelevant features
The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo...
ICML
2005
IEEE
16 years 7 months ago
Clustering through ranking on manifolds
Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-...
Markus Breitenbach, Gregory Z. Grudic
3DIM
1999
IEEE
15 years 11 months ago
Large Data Sets and Confusing Scenes in 3-D Surface Matching and Recognition
In this paper, we report on recent extensions to a surface matching algorithm based on local 3-D signatures. This algorithm was previously shown to be effective in view registrati...
Owen T. Carmichael, Daniel F. Huber, Martial Heber...
COLT
1992
Springer
15 years 10 months ago
Polynomial Time Inference of a Subclass of Context-Free Transformations
This paper deals with a class of Prolog programs, called context-free term transformations (CFT). We present a polynomial time algorithm to identify a subclass of CFT, whose progr...
Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara
EMNLP
2006
15 years 8 months ago
Semantic Role Labeling via Instance-Based Learning
This paper demonstrates two methods to improve the performance of instancebased learning (IBL) algorithms for the problem of Semantic Role Labeling (SRL). Two IBL algorithms are u...
Chi-san Althon Lin, Tony C. Smith