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» Learning Generative Models with the Up-Propagation Algorithm
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ICML
2010
IEEE
15 years 5 months ago
Non-Local Contrastive Objectives
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...
David Vickrey, Cliff Chiung-Yu Lin, Daphne Koller
KDD
2004
ACM
181views Data Mining» more  KDD 2004»
16 years 5 months ago
Column-generation boosting methods for mixture of kernels
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single...
Jinbo Bi, Tong Zhang, Kristin P. Bennett
AIRS
2009
Springer
15 years 11 months ago
A Latent Dirichlet Framework for Relevance Modeling
Relevance-based language models operate by estimating the probabilities of observing words in documents relevant (or pseudo relevant) to a topic. However, these models assume that ...
Viet Ha-Thuc, Padmini Srinivasan
SIGMOD
2009
ACM
175views Database» more  SIGMOD 2009»
16 years 5 months ago
Keyword search on structured and semi-structured data
Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex a...
Yi Chen, Wei Wang 0011, Ziyang Liu, Xuemin Lin
SIGIR
2005
ACM
15 years 10 months ago
Linear discriminant model for information retrieval
This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate a...
Jianfeng Gao, Haoliang Qi, Xinsong Xia, Jian-Yun N...