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      Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques

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          A comparison of methods for multiclass support vector machines.

          Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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            Learning realistic human actions from movies

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              Behavior Recognition via Sparse Spatio-Temporal Features

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                Author and article information

                Journal
                IEEE Transactions on Instrumentation and Measurement
                IEEE Trans. Instrum. Meas.
                Institute of Electrical and Electronics Engineers (IEEE)
                0018-9456
                1557-9662
                November 2011
                November 2011
                : 60
                : 11
                : 3592-3607
                Article
                10.1109/TIM.2011.2161140
                092f629b-d1c6-4ab5-b260-d60516ed12cd
                © 2011
                Product
                Self URI (article page): http://ieeexplore.ieee.org/document/5983442/

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