Stochastic Analysis of Lexical and Semantic Enhanced Structural Language Model (2008)
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans
Abstract. In this paper, we present a directed Markov random field model that integrates trigram models, structural language models (SLM) and probabilistic latent semantic analysis (PLSA) for the...
Shape Time Discriminative Classification of Video Objects (2008)
Li Cheng, Baochun Bai, Cheng Lei, Dale Schuurmans, Shaojun Wang
We propose a discriminative approach to non-rigid video objects classification. Our goal is to recognize actions of the objects that appear in a video sequence, based on its shape time dynamics. This...
Sentiment Classification (2008)
Shane Bergsma, Darryl Jung, Rejean Lau, Yunping Wang, Dr. Shaojun Wang
An Online Discriminative Approach to Background Subtraction (2008)
Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli
We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that is able to cope with...
Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Caelli
We present two new algorithms for online learning in reproducing kernel Hilbert spaces. Our first algorithm, ILK (implicit online learning with kernels), employs a new, implicit update technique that...
Fuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang
We present a method for computerassisted authorship attribution based on character-level n-gram language models. Our approach is based on simple information theoretic principles, and achieves...
Fuchun Peng, Xiangji Huang, Dale Schuurmans, Shaojun Wang
We present a simple approach for Asian language text classification without word segmentation, based on statistical n-gram language modeling. In particular, we examine Chinese and Japanese text...
Boltzmann Machine Learning with the Latent Maximum Entropy Principle (2007)
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is dierent both from...
Abstract OPTIMAL BATCHING AND SHIPMENT CONTROL IN A SINGLE-STAGE SUPPLY CHAIN SYSTEM (2007)
This research studies the single-stage supply chain system operated under JIT technique. A kanban mechanism is employed to assist in linking the production processes in a supply chain system. The...
Text Classification in Asian Languages without Word Segmentation (2007)
Fuchun Peng, Fuchun Schuurmans, Dale Schuurmans, Xiangji Huang, Shaojun Wang
We present a simple approach for Asian language text classification without word segmentation, based on statistical language modeling. In particular, we examine Chinese and Japanese text...
Recursive estimation of timevarying environments for robust speech recognition (2007)
Yunxin Zhao, Shaojun Wang, Kuan-chieh Yen
An EM-type of recursive estimation algorithm is formulated in the DFT domain for joint estimation of time-varying parameters of distortion channel and additive noise from online degraded speech....
Boltzmann Machine Learning with the Latent Maximum Entropy Principle (2007)
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is dierent both from...
Learning to model spatial dependency: Semi-supervised discriminative random fields (2006)
Chi-hoon Lee, Feng Jiao, Shaojun Wang, Dale Schuurmans, Russell Greiner
We present a novel, semi-supervised approach to training discriminative random fields (DRFs) that efficiently exploits labeled and unlabeled training data to achieve improved accuracy in a variety of...
Using query-specific variance estimates to combine Bayesian classifiers (2006)
Chi-hoon Lee, Russ Greiner, Shaojun Wang
Many of today’s best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a novel “query specific...
Learning to model spatial dependency: Semi-supervised discriminative random fields (2006)
Chi-hoon Lee, Feng Jiao, Shaojun Wang, Dale Schuurmans, Russell Greiner
We present a new, semi-supervised extension of discriminative random fields (DRFs) that efficiently exploits labeled and unlabeled training data to achieve improved accuracy in a variety of image...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng
We present a directed Markov random field (MRF) model that combines n-gram models, probahilistic context free grammars (l'C FGs) and probabilistic latent semantic analysis (PLSA) for the purpose...
Combining Statistical Language Models via the Latent Maximum Entropy Principle (2005)
Shaojun Wang Swang, Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
In this paper, we present a unified probabilistic framework for statistical language modeling which can simultaneously incorporate various aspects of natural language, such as local word interaction,...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng
We present a directed Markov random field (MRF) model that combines ¢-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) for the purpose of...
Variational bayesian image modelling (2005)
Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models—Hidden Markov Random Fields (HM-RFs). HMRFs are...
Variational bayesian image modelling (2005)
Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models—Hidden Markov Random Fields (HM-RFs). HMRFs are...
Language and task independent text categorization with simple language models (2003)
Fuchun Peng, Dale Schuurmans, Shaojun Wang
We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple information theoretic...
Latent maximum entropy approach for semantic n-gram language modeling (2003)
Shaojun Wang, Dale Schuurmans, Fuchun Peng
In this paper, we describe a unied probabilistic framework for statistical language modeling|the latent maximum entropy principle|which can eectively incorporate various aspects of natural language,...
Semantic n-gram language modeling with the latent maximum entropy principle (2003)
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
In this paper, we describe a unied probabilistic framework for statistical language modeling|the latent maximum entropy principle|which can eectively incorporate various aspects of natural language,...
Augmenting Naive Bayes Classifiers with Statistical Language Models (2003)
Fuchun Peng, Dale Schuurmans, Shaojun Wang
We augment naive Bayes models with statistical n-gram language models to address shortcomings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier
Latent maximum entropy approach for semantic n-gram language modeling (2003)
Shaojun Wang, Dale Schuurmans, Fuchun Peng
In this paper, we describe a unied probabilistic framework for statistical language modeling|the latent maximum entropy principle|which can eectively incorporate various aspects of natural language,...
Language independent authorship attribution using character level language models (2003)
Fuchun Peng, Dale Schuurmans, Viado Keselj, Shaojun Wang
We present a method for computerassisted authorship attribution based on character-level-gram language models. Our approach is based on simple information theoretic principles, and achieves improved...
Language and Task Independent Text Categorization (2003)
With Simple Language, Fuchun Peng, Dale Schuurmans, Shaojun Wang
We present a simple method for language independent and task independent text categorization learning, based on character-level n-gram language models. Our approach uses simple information theoretic...
Learning Mixture Models with the Latent Maximum Entropy Principle (2003)
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin Zhao
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes' maximum...
Learning Mixture Models with the Latent Maximum Entropy Principal (2003)
Shaojun Wang, Dale Schuurmans, Fuchun Peng
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes ’ maximum...
Language independent authorship attribution using character level language models (2003)
Fuchun Peng, Dale Schuurmans, Vlado Keselj, Shaojun Wang
We present a method for computerassisted authorship attribution based on character-level ¤-gram language models. Our approach is based on simple information theoretic principles, and achieves...
Predicting Oral Reading Miscues (2002)
Jack Mostow, Joseph Beck, S. Vanessa Winter, Shaojun Wang, Brian Tobin
This paper explores the problem of predicting specific reading mistakes, called miscues, on a given word. Characterizing likely miscues tells an automated reading tutor what to anticipate, detect,...
The latent maximum entropy principle (2002)
Shaojun Wang, Dale Schuurmans, Yunxin Zhao
We present an extension to Jaynes ' maximum entropy principle that handles latent variables. The principle of latent maximum entropy we propose is di#erent from both Jaynes ' maximum...
This paper presents a new recursive Bayesian learning approach for transformation parameter estimation in speaker adaptation. Our goal is to incrementally transform (or adapt) the entire set of HMM...
Resource planning and a depot location model for electric power restoration
Wang, Shaojun, Sarker, Bhaba R., Mann, Lawrence, Triantaphyllou, Evangelos
Almost sure convergence of Titterington's recursive estimator for mixture models
Titterington proposed a recursive parameter estimation algorithm for finite mixture models. However, due to the well known problem of singularities and multiple maximum, minimum and saddle points...