Boosting the margin: A new explanation for the effectiveness of voting methods
- R. Schapire, Y. Freund, Peter Barlett, Wee Sun Lee
- Computer ScienceInternational Conference on Machine Learning
- 8 July 1997
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.
SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces
- H. Kurniawati, David Hsu, Wee Sun Lee
- Computer ScienceRobotics: Science and Systems
- 25 June 2008
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.
Building text classifiers using positive and unlabeled examples
- B. Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip S. Yu
- Computer ScienceThird IEEE International Conference on Data…
- 19 November 2003
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.
Question classification using support vector machines
- Dell Zhang, Wee Sun Lee
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 28 July 2003
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.
An Unsupervised Neural Attention Model for Aspect Extraction
- Ruidan He, Wee Sun Lee, H. Ng, Daniel Dahlmeier
- Computer ScienceAnnual Meeting of the Association for…
- 2017
A novel neural approach that improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings, and uses an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects.
Partially Supervised Classification of Text Documents
- B. Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li
- Computer ScienceInternational Conference on Machine Learning
- 8 July 2002
This paper shows that the problem of identifying documents from a set of documents of a particular topic or class P and a large set M of mixed documents, and that under appropriate conditions, solutions to the constrained optimization problem will give good solution to the partially supervised classification problem.
DESPOT: Online POMDP Planning with Regularization
- A. Somani, N. Ye, David Hsu, Wee Sun Lee
- Computer ScienceNIPS
- 5 December 2013
This paper presents an online POMDP algorithm that alleviates these difficulties by focusing the search on a set of randomly sampled scenarios, and gives an output-sensitive performance bound for all policies derived from a DESPOT, and shows that R-DESPOT works well if a small optimal policy exists.
Convolutional Sequence to Sequence Model for Human Dynamics
- Chen Li, Zhen Zhang, Wee Sun Lee, Gim Hee Lee
- Computer ScienceIEEE/CVF Conference on Computer Vision and…
- 2 May 2018
This work presents a novel approach to human motion modeling based on convolutional neural networks (CNN), which is able to capture both invariant and dynamic information of human motion, which results in more accurate predictions.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
- Ruidan He, Wee Sun Lee, H. Ng, Daniel Dahlmeier
- Computer ScienceAnnual Meeting of the Association for…
- 17 June 2019
An interactive multi-task learning network (IMN) is proposed which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level and introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables.
Planning under Uncertainty for Robotic Tasks with Mixed Observability
- S. W. Ong, S. Png, David Hsu, Wee Sun Lee
- Computer ScienceInt. J. Robotics Res.
- 1 July 2010
A factored model is used to represent separately the fully and partially observable components of a robot’s state and derive a compact lower-dimensional representation of its belief space that can be combined with any point-based algorithm to compute approximate POMDP solutions.
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