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NN
2000
Springer
161views Neural Networks» more  NN 2000»
13 years 4 months ago
How good are support vector machines?
Support vector (SV) machines are useful tools to classify populations characterized by abrupt decreases in density functions. At least for one class of Gaussian data model the SV ...
Sarunas Raudys
KES
2000
Springer
13 years 8 months ago
Multi-view face detection using support vector machines and eigenspace modelling
An approach to multi-view face detection based on head pose estimation is presented in this paper. Support Vector Regression is employed to solve the problem of pose estimation. T...
Yongmin Li, Shaogang Gong, Jamie Sherrah, Heather ...
BMCBI
2006
110views more  BMCBI 2006»
13 years 4 months ago
Bias in error estimation when using cross-validation for model selection
Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers...
Sudhir Varma, Richard Simon
TNN
2010
143views Management» more  TNN 2010»
12 years 11 months ago
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
ML
2006
ACM
109views Machine Learning» more  ML 2006»
13 years 4 months ago
Cost curves: An improved method for visualizing classifier performance
Abstract This paper introduces cost curves, a graphical technique for visualizing the performance (error rate or expected cost) of 2-class classifiers over the full range of possib...
Chris Drummond, Robert C. Holte