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A fast method for training support vector machines with a very large set of linear features
Current systems for object detection often use support vector machines (SVM) as the basic classification algorithm. A rather common case is to compute a small set of linear features and then train the classifier on these features. We present a fast method to train and evaluate SVM with many linear f...
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Main Authors: | , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | Current systems for object detection often use support vector machines (SVM) as the basic classification algorithm. A rather common case is to compute a small set of linear features and then train the classifier on these features. We present a fast method to train and evaluate SVM with many linear features and show results for face detection using a set of 210400 features. The resulting classifier is both more accurate and faster than a classifier trained on raw pixel features, which total up only to 576 features in our case. |
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DOI: | 10.1109/ICME.2002.1035780 |