Results 21 to 30 of about 7,794,647 (291)
The support vector decomposition machine [PDF]
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006 In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learning performance. In previous work, many researchers have treated the learning problem in two separate phases: first use an algorithm such as singular value decomposition to ...
Francisco Pereira 0001 +1 more
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Robust ASR using Support Vector Machines [PDF]
, 2007 The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important
F. Díaz-de-María +17 more
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Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. [PDF]
, 2009 Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Kim, Hyun-chul +7 more
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Study on Privacy-preserving Nonlinear Federated Support Vector Machines [PDF]
Jisuanji kexue, 2022 Federated learning offers new ideas for solving the problem of multiparty joint modeling in “data silos”.Federated support vector machines can realize cross-device support vector machine modeling without local data,but the existing research has some ...
YANG Hong-jian, HU Xue-xian, LI Ke-jia, XU Yang, WEI Jiang-hong
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Oblique Support Vector Machines
Informatica, 2005 In this paper we propose a modified framework of support vector machines, called Oblique Support Vector Machines(OSVMs), to improve the capability of classification. The principle of OSVMs is joining an orthogonal vector into weight vector in order to rotate the support hyperplanes.
Chih-Chia Yao, Pao-Ta Yu
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Spatio-temporal avalanche forecasting with Support Vector Machines [PDF]
Natural Hazards and Earth System Sciences, 2011 This paper explores the use of the Support Vector Machine (SVM) as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches.
A. Pozdnoukhov +3 more
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RSVM: Reduced Support Vector Machines [PDF]
, 2001 An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation.
Olvi L. Mangasarian +3 more
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Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines [PDF]
, 2016 submitted
Xiaomin Qi +2 more
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Sparse Deconvolution Using Support Vector Machines
EURASIP Journal on Advances in Signal Processing, 2008 Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal +5 more
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Breakdown Point of Robust Support Vector Machines
Entropy, 2017 Support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has the serious drawback that it is sensitive to outliers in training samples.
Takafumi Kanamori +2 more
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