ACL
2012
12 years 2 months ago
2012
An ideal summarization system should produce summaries that have high content coverage and linguistic quality. Many state-ofthe-art summarization systems focus on content coverage...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
This paper proposes a Bayesian algorithm to estimate the parameters of a smooth transition regression model. With in this model, time series are divided into segments and a linear...
PRL
2010
13 years 7 months ago
2010
The general setting of regression analysis is to identify a relationship between a response variable Y and one or several explanatory variables X by using a learning sample. In a ...
SENSYS
2010
ACM
13 years 10 months ago
2010
ACM
Many participatory sensing applications use data collected by participants to construct a public model of a system or phenomenon. For example, a health application might compute a...
NAACL
2010
13 years 10 months ago
2010
Open-class semantic lexicon induction is of great interest for current knowledge harvesting algorithms. We propose a general framework that uses patterns in bootstrapping fashion ...
ACL
2010
13 years 10 months ago
2010
Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we formulate extractive summariza...
TIP
2010
13 years 10 months ago
2010
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
MA
2010
Springer
13 years 10 months ago
2010
Springer
We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analy...
SDM
2008
SIAM
14 years 1 months ago
2008
SIAM
This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed inferencing, data compact...
DIS
2006
Springer
14 years 2 months ago
2006
Springer
Knowledge discovery systems are constrained by three main limited resources: time, memory and sample size. Sample size is traditionally the dominant limitation, but in many present...