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» Introduction to Randomized Algorithms
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215
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ICIP
2010
IEEE
15 years 1 months ago
Building Emerging Pattern (EP) Random forest for recognition
The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree...
Liang Wang, Yizhou Wang, Debin Zhao
ASPDAC
2009
ACM
115views Hardware» more  ASPDAC 2009»
15 years 10 months ago
Incremental and on-demand random walk for iterative power distribution network analysis
— Power distribution networks (PDNs) are designed and analyzed iteratively. Random walk is among the most efficient methods for PDN analysis. We develop in this paper an increme...
Yiyu Shi, Wei Yao, Jinjun Xiong, Lei He
147
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MIR
2005
ACM
140views Multimedia» more  MIR 2005»
15 years 9 months ago
Multiple random walk and its application in content-based image retrieval
In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by mea...
Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying M...
173
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BMCBI
2007
147views more  BMCBI 2007»
15 years 3 months ago
Bias in random forest variable importance measures: Illustrations, sources and a solution
Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and relate...
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeile...
137
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ICDM
2010
IEEE
108views Data Mining» more  ICDM 2010»
15 years 1 months ago
Assessing Data Mining Results on Matrices with Randomization
Abstract--Randomization is a general technique for evaluating the significance of data analysis results. In randomizationbased significance testing, a result is considered to be in...
Markus Ojala