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» Iterated importance sampling in missing data problems
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KDD
2012
ACM
238views Data Mining» more  KDD 2012»
13 years 1 months ago
Multi-source learning for joint analysis of incomplete multi-modality neuroimaging data
Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer’s Disease Neuroimaging Initiativ...
Lei Yuan, Yalin Wang, Paul M. Thompson, Vaibhav A....
VLDB
2001
ACM
114views Database» more  VLDB 2001»
15 years 3 months ago
Distinct Sampling for Highly-Accurate Answers to Distinct Values Queries and Event Reports
Estimating the number of distinct values is a wellstudied problem, due to its frequent occurrence in queries and its importance in selecting good query plans. Previous work has sh...
Phillip B. Gibbons
HPCC
2007
Springer
15 years 5 months ago
A Data Imputation Model in Sensor Databases
Data missing is a common problem in database query processing, which can cause bias or lead to inefficient analyses, and this problem happens more often in sensor databases. The re...
Nan Jiang
BIOCOMP
2008
15 years 10 days ago
Reverse Engineering Module Networks by PSO-RNN Hybrid Modeling
Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reas...
Yuji Zhang, Jianhua Xuan, Benildo de los Reyes, Ro...
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CSL
2010
Springer
14 years 11 months ago
Improving supervised learning for meeting summarization using sampling and regression
Meeting summarization provides a concise and informative summary for the lengthy meetings and is an effective tool for efficient information access. In this paper, we focus on ext...
Shasha Xie, Yang Liu