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» Semi-Supervised Support Vector Machines
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NIPS
2000
13 years 11 months ago
From Margin to Sparsity
We present an improvement of Noviko 's perceptron convergence theorem. Reinterpreting this mistakebound as a margindependent sparsity guarantee allows us to give a PAC{style ...
Thore Graepel, Ralf Herbrich, Robert C. Williamson
ISM
2005
IEEE
138views Multimedia» more  ISM 2005»
14 years 3 months ago
Investigation of Combining SVM and Decision Tree for Emotion Classification
This paper discusses the use of a combination of support vector machine and decision tree learning for recognizing four emotions in speech, which are Neutral, Angry, Lombard, and ...
Thao Nguyen, Mingkun Li, Iris Bass, Ishwar K. Seth...
AMFG
2003
IEEE
157views Biometrics» more  AMFG 2003»
14 years 3 months ago
Inference of Human Postures by Classification of 3D Human Body Shape
In this paper we describe an approach for inferring the body posture using a 3D visual-hull constructed from a set of silhouettes. We introduce an appearance-based, view-independe...
Isaac Cohen, Hongxia Li
SCAI
2008
13 years 11 months ago
Defect Prediction in Hot Strip Rolling Using ANN and SVM
One of the largest factors affecting the loss for steel manufacturing are defects in the steel strips produced. Therefore the prediction of these defects forehand would be very im...
Manu Hietaniemi, Ulla Elsilä, Perttu Laurinen...
NIPS
2003
13 years 11 months ago
Margin Maximizing Loss Functions
Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
Saharon Rosset, Ji Zhu, Trevor Hastie