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ICANN
2007
Springer
15 years 9 months ago
Resilient Approximation of Kernel Classifiers
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
Thorsten Suttorp, Christian Igel
SCAI
2008
15 years 6 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
2004
15 years 6 months ago
Face Detection - Efficient and Rank Deficient
This paper proposes a method for computing fast approximations to support vector decision functions in the field of object detection. In the present approach we are building on an...
Wolf Kienzle, Gökhan H. Bakir, Matthias O. Fr...
152
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TREC
2004
15 years 6 months ago
Experience of Using SVM for the Triage Task in TREC 2004 Genomics Track
This paper reports our knowledge-ignorant machine learning approach to the triage task in TREC2004 genomics track, which is actually a text categorization problem. We applied Supp...
Dell Zhang, Wee Sun Lee
NIPS
2003
15 years 6 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