Results 281 to 290 of about 212,260 (313)
Some of the next articles are maybe not open access.
2007
We devise an SVM for partitioning a sample space affected by random binary labels. In the hypothesis that a smooth, possibly symmetric, conditional label distribution graduates the passage from the all 0-label domain to the all 1-label domain and under other regularity conditions, the algorithm supplies an estimate of the above probabilities.
B. Apolloni, S. Bassis, D. Malchiodi
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We devise an SVM for partitioning a sample space affected by random binary labels. In the hypothesis that a smooth, possibly symmetric, conditional label distribution graduates the passage from the all 0-label domain to the all 1-label domain and under other regularity conditions, the algorithm supplies an estimate of the above probabilities.
B. Apolloni, S. Bassis, D. Malchiodi
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Recognition of Emotion with SVMs
2006In recent years, several methods on human emotion recognition have been published. In this paper, we proposed a scheme that applied the emotion classification technique for emotion recognition. The emotion classification model is Support Vector Machines (SVMs).
Zhi Teng, Fuji Ren, Shingo Kuroiwa
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A Walk from 2-Norm SVM to 1-Norm SVM
2009 Ninth IEEE International Conference on Data Mining, 2009This paper studies how useful the standard 2-norm regularized SVM is in approximating the 1-norm SVM problem. To this end, we examine a general method that is based on iteratively re-weighting the features and solving a 2-norm optimization problem. The convergence rate of this method is unknown.
Jussi Kujala, Timo Aho, Tapio Elomaa
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Enhancing SVM with Visualization
2004Understanding the result produced by a data-mining algorithm is as important as the accuracy. Unfortunately, support vector machine (SVM) algorithms provide only the support vectors used as “black box” to efficiently classify the data with a good accuracy. This paper presents a cooperative approach using SVM algorithms and visualization methods to gain
Thanh-Nghi Do, François Poulet
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Differentiation of Syndromes with SVM
2006Differentiation of syndromes is the kernel theory of Traditional Chinese Medicine (TCM). How to diagnose syndromes correctly with scientific means according to symptoms is the first problem in TCM. Several modern approaches have been applied, but no satisfied results have been obtained because of the complexity of diagnosis procedure.
Zhanquan Sun +2 more
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Accelerate SVM Training with OHD-SVM on GPU
2023Chao-Chin Wu, De-Xang Wang, Lien Fu Lai
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2005
In this paper we propose a simple and intuitive method for constructing partially linear models and, in general, partially parametric models, using support vector machines for regression and, in particular, using regularization networks (splines). The results are more satisfactory than those for classical nonparametric approaches.
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In this paper we propose a simple and intuitive method for constructing partially linear models and, in general, partially parametric models, using support vector machines for regression and, in particular, using regularization networks (splines). The results are more satisfactory than those for classical nonparametric approaches.
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2013
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???????????????????? ???????????????????????? ?????????????????????? SVM-???????????? ?????????????? ????????????????, ?????????????????? ?????????????? ?????????????? ?????????????? ???????????????? ??????????????. ???????????? ???????????????????? ?????????????????????????? ???? ???????????? ?????????????????????? ???????????????? ????????????????????
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2001
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-
Poggio, Tomaso A. +5 more
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We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-
Poggio, Tomaso A. +5 more
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