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DAWAK
2010
Springer
15 years 2 months ago
Using Transitivity to Increase the Accuracy of Sample-Based Pearson Correlation Coefficients
Abstract. Pearson product-moment correlation coefficients are a wellpracticed quantification of linear dependence seen across many fields. When calculating a sample-based correlati...
Taylor Phillips, Chris GauthierDickey, Ramki Thuri...
BMCBI
2006
165views more  BMCBI 2006»
15 years 1 months ago
Improved variance estimation of classification performance via reduction of bias caused by small sample size
Background: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm tha...
Ulrika Wickenberg-Bolin, Hanna Göransson, M&a...
BMCBI
2010
208views more  BMCBI 2010»
15 years 1 months ago
A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data
Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are common...
Pengyi Yang, Bing Bing Zhou, Zili Zhang, Albert Y....
ICASSP
2009
IEEE
15 years 8 months ago
Unsupervised acoustic and language model training with small amounts of labelled data
We measure the effects of a weak language model, estimated from as little as 100k words of text, on unsupervised acoustic model training and then explore the best method of using ...
Scott Novotney, Richard M. Schwartz, Jeff Ma
UAI
2003
15 years 2 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...