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- Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee
- ICML
- 1997

One of the surprising recurring phenomena observed in experiments with boosting is 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. In this paper, we show that this phenomenon is related to the distribution of margins of the… (More)

- Dell Zhang, Wee Sun Lee
- SIGIR
- 2003

Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with five machine learning algorithms: Nearest Neighbors (NN), Naive Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector… (More)

- Hanna Kurniawati, David Hsu, Wee Sun Lee
- Robotics: Science and Systems
- 2008

— Motion planning in uncertain and dynamic environments is an essential capability for autonomous robots. Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for solving such problems, but they are often avoided in robotics due to high computational complexity. Our goal is to create practical POMDP algorithms… (More)

- Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip S. Yu
- ICDM
- 2003

This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each… (More)

- Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li
- ICML
- 2002

We investigate the following problem: Given a set of documents of a particular topic or class È , and a large set Å of mixed documents that contains documents from class È and other types of documents, identify the documents from class È in Å. The key feature of this problem is that there is no labeled non-È document, which makes traditional machine… (More)

- Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun Lee
- I. J. Robotics Res.
- 2010

Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic… (More)

- Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
- IEEE Trans. Information Theory
- 1996

We show that the class of two layer neural networks with bounded fan-in is eeciently learn-able in a realistic extension to the Probably Approximately Correct (PAC) learning model. In this model, a joint probability distribution is assumed to exist on the observations and the learner is required to approximate the neural network which minimizes the expected… (More)

- Wei Lu, Hwee Tou Ng, Wee Sun Lee, Luke S. Zettlemoyer
- EMNLP
- 2008

In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. The model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. We introduce dynamic programming techniques for efficient… (More)

- Wee Sun Lee, Bing Liu
- ICML
- 2003

The problem of learning with positive and unla-beled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labeling all unla-beled examples as negative and use a linear function to learn from the noisy examples. To learn a linear function with noise, we perform logistic regression after… (More)

- Bing Liu, Xiaoli Li, Wee Sun Lee, Philip S. Yu
- AAAI
- 2004

Traditionally, text classifiers are built from labeled training examples. Labeling is usually done manually by human experts (or the users), which is a labor intensive and time consuming process. In the past few years, researchers investigated various forms of semi-supervised learning to reduce the burden of manual labeling. In this paper, we propose a… (More)