W
Wee Sun Lee
Researcher at National University of Singapore
Publications - 203
Citations - 16034
Wee Sun Lee is an academic researcher from National University of Singapore. The author has contributed to research in topics: Partially observable Markov decision process & Markov decision process. The author has an hindex of 49, co-authored 191 publications receiving 14153 citations. Previous affiliations of Wee Sun Lee include Australian Defence Force Academy & University of New South Wales.
Papers
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Proceedings Article
Boosting the margin: A new explanation for the effectiveness of voting methods
TL;DR: In this paper, the authors show that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero.
Journal ArticleDOI
Boosting the margin: a new explanation for the effectiveness of voting methods
TL;DR: It is shown that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error.
Proceedings ArticleDOI
SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces
TL;DR: This work has developed a new point-based POMDP algorithm that exploits the notion of optimally reachable belief spaces to improve com- putational efficiency and substantially outperformed one of the fastest existing point- based algorithms.
Proceedings ArticleDOI
Building text classifiers using positive and unlabeled examples
TL;DR: A more principled approach to solving the problem of building text classifiers using positive and unlabeled examples based on a biased formulation of SVM is proposed, and it is shown experimentally that it is more accurate than the existing techniques.
Proceedings ArticleDOI
Question classification using support vector machines
Dell Zhang,Wee Sun Lee +1 more
TL;DR: This paper proposes to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions, and describes how the tree Kernel can be computed efficiently by dynamic programming.