886 search results - page 53 / 178 » Adaptive sampling for quickselect |
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Sampling is a popular way of scaling up machine learning algorithms to large datasets. The question often is how many samples are needed. Adaptive stopping algorithms monitor the ...
WSC
2008
13 years 10 months ago
2008
We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we ...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive threshol...
PAMI
2010
13 years 6 months ago
2010
In this paper, we present a new Adaptive Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANSAC (R...
SDM
2003
SIAM
13 years 9 months ago
2003
SIAM
This paper presents a general framework for adapting any generative (model-based) clustering algorithm to provide balanced solutions, i.e., clusters of comparable sizes. Partition...