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» Support vector machine for functional data classification
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CIVR
2006
Springer
121views Image Analysis» more  CIVR 2006»
15 years 10 months ago
Finding Faces in Gray Scale Images Using Locally Linear Embeddings
The problem of face detection remains challenging because faces are non-rigid objects that have a high degree of variability with respect to head rotation, illumination, facial exp...
Samuel Kadoury, Martin D. Levine
AUSAI
2008
Springer
15 years 8 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan
280
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GCB
2010
Springer
204views Biometrics» more  GCB 2010»
15 years 4 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...
ECML
2004
Springer
15 years 11 months ago
Improving Random Forests
Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise,...
Marko Robnik-Sikonja
AMAI
2004
Springer
15 years 11 months ago
Biased Minimax Probability Machine for Medical Diagnosis
The Minimax Probability Machine (MPM) constructs a classifier, which provides a worst-case bound on the probability of misclassification of future data points based on reliable ...
Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. ...