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» Variable selection using random forests
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SAC
2011
ACM
14 years 4 months ago
Stochastic matching pursuit for Bayesian variable selection
This article proposes a stochastic version of the matching pursuit algorithm for Bayesian variable selection in linear regression. In the Bayesian formulation, the prior distributi...
Ray-Bing Chen, Chi-Hsiang Chu, Te-You Lai, Ying Ni...
158
Voted
SIGMOD
2006
ACM
116views Database» more  SIGMOD 2006»
15 years 9 months ago
Fast range-summable random variables for efficient aggregate estimation
Exact computation for aggregate queries usually requires large amounts of memory ? constrained in data-streaming ? or communication ? constrained in distributed computation ? and ...
Florin Rusu, Alin Dobra
MCS
2004
Springer
15 years 3 months ago
A Comparison of Ensemble Creation Techniques
We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multipl...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
ICC
2007
IEEE
121views Communications» more  ICC 2007»
15 years 4 months ago
BER Analysis in A Generalized UWB Frequency Selective Fading Channel With Randomly Arriving Clusters and Rays
— In this paper, we present an analytical method to evaluate the bit error rate (BER) of the ultra-wideband (UWB) system in the IEEE 802.15.4a standardized channel model. The IEE...
Wei-Cheng Liu, Li-Chun Wang
ECCV
2010
Springer
14 years 10 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof