Cooled and Relaxed Survey Propagation for MRFs (2008)
Chieu, Hai Leong, Lee, Wee Sun, Teh, Yee Whye
We describe a new algorithm, Relaxed Survey Propagation (RSP), for finding MAP configurations in Markov random fields. We compare its performance with state-of-the-art algorithms including the...
HSU, David, LEE, Wee Sun, RONG, Nan
Point-based approximation algorithms have drastically im-proved the speed of POMDP planning. This paper presents a new point-based POMDP algorithm called SARSOP. Like earlier point-based algorithms,...
HSU, David, LEE, Wee Sun, RONG, Nan
Point-based approximation algorithms have drastically im-proved the speed of POMDP planning. This paper presents a new point-based POMDP algorithm called SARSOP. Like earlier point-based algorithms,...
Semi-supervised Learning in Reproducing Kernel Hilbert Spaces Using Local Invariances (2006)
LEE, Wee Sun, ZHANG, Xinhua, TEH, Yee Whye
We propose a framework for semi-supervised learning in reproducing kernel Hilbert spaces using local invariances that explicitly characterize the behavior of the target function around both labeled...
Activity Recognition from Physiological Data using Conditional Random Fields (2005)
Chieu, Hai Leong, Lee, Wee Sun, Kaelbling, Leslie P.
We describe the application of conditional random fields (CRF) to physiological data modeling for the application of activity recognition. We use the data provided by the Physiological Data Modeling...
Activity Recognition from Physiological Data using Conditional Random Fields (2005)
Chieu, Hai Leong, Lee, Wee Sun, Kaelbling, Leslie P.
We describe the application of conditional random fields (CRF) to physiological data modeling for the application of activity recognition. We use the data provided by the Physiological Data Modeling...
Validating Co-Training Models for Web Image Classification (2004)
Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known...
Validating Co-Training Models for Web Image Classification (2004)
Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known...
Building Text Classifiers Using Positive and Unlabeled Examples (2004)
Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip S. Yu
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...
Web Taxonomy Integration using Support (2004)
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web....
Using Link Analysis to Improve Layout on Mobile Devices (2004)
Delivering web pages to mobile phones or personal digital assistants has become possible with the latest wireless technology. However, mobile devices have very small screen sizes and memory...
Web Taxonomy Integration using Support Vector Machines (2004)
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web....
Abnormality Detection in Retinal Images (2003)
Yu, Xiaoxue, Hsu, Wynne, Lee, Wee Sun, Lozano-Pérez, Tomás
The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians....
On Web Taxonomy Integration (2003)
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only pervasive on the nowadays web, but also important to the emerging semantic web. A...
On Web Taxonomy Integration (2003)
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only pervasive on the nowadays web, but also important to the emerging semantic web. A...
Abnormality Detection in Retinal Images (2003)
Yu, Xiaoxue, Hsu, Wynne, Lee, Wee Sun, Lozano-Pérez, Tomás
The implementation of data mining techniques in the medical area has generated great interest because of its potential for more efficient, economic and robust performance when compared to physicians....
Co-training and Learning with Noise (2003)
Blum and Mitchell introduced a model of learning in (Blum & Mitchell, 1998) where each instance X is composed of two views (X 1 ; X 2 ) with X 1 conditionally independent of X 2 given the label of X...
Learning with Positive and Unlabeled Examples Using (2003)
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications.
Web based Pattern Mining and Matching Approach to Question Answering (2003)
We describe herein a Web based pattern mining and matching approach to question answering. For each type of questions, a lot of textual patterns can be learned from the Web automatically, using the...
Learning with Positive and Unlabeled Examples Using (2003)
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications.
A Web-based Question Answering System (2003)
The Web is apparently an ideal source of answers to a large variety of questions, due to the tremendous amount of information available online. This paper describes a Web-based question answering...
A Web-based Question Answering System (2003)
The Web is apparently an ideal source of answers to a large variety of questions, due to the tremendous amount of information available online. This paper describes a Web-based question answering...
Partially Supervised Classification of Text Documents (2002)
Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li
We investigate the following problem: Given a set of documents of a particular topic or class # , and a large set # of mixed documents that contains documents from class # and other types of...
A Theoretical Analysis of Query Selection for Collaborative (2002)
Sanjoy Dasgupta, Wee Sun Lee, Philip M. Long
We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple algorithm for...
Partially Supervised Classification of Text Documents (2002)
Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li
We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of...
Partially Supervised Classification of Text Documents (2002)
Bing Liu, Wee Sun Lee, Philip S. Yu, Xiaoli Li
We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of...
6.046J / 18.410J Introduction to Algorithms, Fall 2001 (2001)
Demaine, Erik D., Leiserson, Charles Eric, Lee, Wee Sun
Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming;...
6.046J / 18.410J Introduction to Algorithms, Fall 2001 (2001)
Demaine, Erik D., Leiserson, Charles Eric, Lee, Wee Sun
Techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming;...
A Theoretical Analysis of Query Selection for Collaborative Filtering (2001)
We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple and fast...
A Theoretical Analysis of Query Selection for Collaborative Filtering (2001)
. We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple and fast...
Collaborative Learning for Recommender Systems (2001)
Recommender systems use ratings from users on items such as movies and music for the purpose of predicting the user preferences on items that have not been rated. Predictions are normally done by...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (2001)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (2000)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Online Clustering for Collaborative Filtering (2000)
We study two online clustering methods for collaborative filtering. In the first method, we assume that each user is equally likely to belong to one of m clusters of users and that the user's rating...
Online Clustering for Collaborative Filtering (2000)
We study two online clustering methods for collaborative filtering. In the first method, we assume that each user is equally likely to belong to one of m clusters of users and that the user's rating...
Trees, Windows and Tiles for Wavelet Image Compression (2000)
We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In an earlier paper, we introduced a class of probability models...
Trees, Windows and Tiles for Wavelet Image Compression (2000)
We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In an earlier paper, we introduced a class of probability models...
Trees, Windows and Tiles for Wavelet Image (2000)
We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In an earlier paper, we introduced a class of probability models...
Tiling and Adaptive Image Compression (2000)
We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In this task, using a larger number of regions would result in...
Compressing as Well as the Best Tiling of an Image (1999)
We investigate the task of compressing an image using different probability models for different regions of the image. In this task, using a larger number of regions would result in better...
Windowing versus Best Tiling for Wavelet Image Compression (1999)
We compare two methods for entropy coding uniformly quantized wavelet coefficients: windowing,which uses local statistics to adapt the probability assignment and best tiling, which aims to compress...
Edge Adaptive Prediction for Lossless Image Coding (1999)
We design an edge adaptive predictor for lossless image coding. The predictor adaptively weights four directional predictor together with an adaptive linear predictor based on information from...
Generalization in decision trees and DNF: Does size matter? (1999)
Mostefa Golea, Peter L. Bartlett, Wee Sun Lee
Recent theoretical results for pattern classification with thresholded real-valued functions (such as support vector machines, sigmoid networks, and boosting) give bounds on misclassification...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1999)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Error Resilience in Video and Multiplexing Layers for Very Low Bit-rate Video Coding Systems (1999)
Wee Sun Lee, Mark R. Pickering, Michael R. Frater, John F. Arnold, Senior Member
The transmission of audio-visual services on low bit-rate, wireless telecommunications systems requires the use of coding techniques that are both efficient in their use of bits and robust against...
Correction to "Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes" (1999)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
The paper [3] gives lower bounds on the VC-dimension of various smoothly parametrized function classes. The results were proven by showing a relationship between the uniqueness of decision boundaries...
Agnostic Learning and Single Hidden Layer Neural Networks (1999)
This thesis is concerned with some theoretical aspects of supervised learning of real-valued functions. We study a formal model of learning called agnostic learning. The agnostic learning model...
Generalization in decision trees and DNF: Does size matter? (1999)
Mostefa Golea, Peter L. Bartlett, Wee Sun Lee
Recent theoretical results for pattern classification with thresholded real-valued functions (such as support vector machines, sigmoid networks, and boosting) give bounds on misclassification...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1999)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes (1999)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We examine the relationship between the VC-dimension and the number of parameters of a thresholded smoothly parametrized function class. We show that the VC-dimension of such a function class is at...
Edge Adaptive Prediction for Lossless Image Coding (1999)
We design an edge adaptive predictor for lossless image coding. The predictor adaptively weights four directional predictor together with an adaptive linear predictor based on information from...
Efficient Agnostic Learning of Neural Networks with Bounded Fan-in (1999)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We show that the class of two layer neural networks with bounded fan-in is efficiently learnable in a realistic extension to the Probably Approximately Correct (PAC) learning model. In this model, a...
Boosting the margin: a new explanation for the effectiveness of voting methods (1998)
Bartlett, Peter, Freund, Yoav, Lee, Wee Sun, Schapire, Robert E.
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...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1998)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated hypothesis usually does not increase as its size becomes very large, and...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1998)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1998)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Boosting the margin: A new explanation for the effectiveness of voting methods (1998)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated hypothesis usually does not increase as its size becomes very large, and...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1998)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
. 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...
Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods (1997)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee
One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated hypothesis usually does not increase as its size becomes very large, and often...
Efficient Agnostic Learning of Neural Networks with Bounded Fan-in (1996)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We show that the class of two layer neural networks with bounded fan-in is efficiently learnable in a realistic extension to the Probably Approximately Correct (PAC) learning model. In this model, a...
Correction to "Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes" (1996)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
The paper [3] gives lower bounds on the VC-dimension of various smoothly parametrized function classes. The results were proven by showing a relationship between the uniqueness of decision boundaries...
The Importance of Convexity in Learning with Squared Loss (1996)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We show that if the closure of a function class F under the metric induced by some probability distribution is not convex, then the sample complexity for agnostically learning F with squared loss...
On Efficient Agnostic Learning of Linear Combinations of Basis Functions (1995)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We consider efficient agnostic learning of linear combinations of basis functions when the sum of absolute values of the weights of the linear combinations is bounded. With the quadratic loss...
On Efficient Agnostic Learning of Linear Combinations of Basis Functions (1995)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We consider efficient agnostic learning of linear combinations of basis functions when the sum of absolute values of the weights of the linear combinations is bounded. With the quadratic loss...
Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes (1970)
Wee Sun Lee, Peter L. Bartlett, Robert C. Williamson
We examine the relationship between the VCdimension and the number of parameters of a smoothly parametrized function class. We show that the VC-dimension of such a function class is at least k if...