Sciweavers

Share
5 search results - page 1 / 1
» A comprehensive comparison of random forests and support vec...
Sort
View
BMCBI
2008
169views more  BMCBI 2008»
9 years 1 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...
ECRIME
2007
9 years 5 months ago
A comparison of machine learning techniques for phishing detection
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting ph...
Saeed Abu-Nimeh, Dario Nappa, Xinlei Wang, Suku Na...
GCB
2010
Springer
204views Biometrics» more  GCB 2010»
8 years 11 months ago
Learning Pathway-based Decision Rules to Classify Microarray Cancer Samples
: Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninfor...
Enrico Glaab, Jonathan M. Garibaldi, Natalio Krasn...
TKDE
2008
123views more  TKDE 2008»
9 years 1 months ago
Explaining Classifications For Individual Instances
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Marko Robnik-Sikonja, Igor Kononenko
CORR
2016
Springer
72views Education» more  CORR 2016»
3 years 9 months ago
Gender Identification using MFCC for Telephone Applications - A Comparative Study
— Gender recognition is an essential component of automatic speech recognition and interactive voice response systems. Determining gender of the speaker reduces the computational...
Jamil Ahmad, Mustansar Fiaz, Soon-il Kwon, Maleera...
books