Results 21 to 30 of about 108,016 (310)
Linear Classification of Data with Support Vector Machines and Generalized Support Vector Machines [PDF]
, 2016 submitted
Xiaomin Qi +2 more
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Wavelet Support Vector Machine [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004 An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis.
Li Zhang 0004, Weida Zhou, Licheng Jiao
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Robust relative margin support vector machines
Journal of Algorithms & Computational Technology, 2017 Recently, a class of classifiers, called relative margin machine, has been developed. Relative margin machine has shown significant improvements over the large margin counterparts on real-world problems.
Yunyan Song +3 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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Extensions of the SVM Method to the Non-Linearly Separable Data [PDF]
Informatică economică, 2013 The main aim of the paper is to briefly investigate the most significant topics of the currently used methodologies of solving and implementing SVM-based classifier.
Luminita STATE +3 more
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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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Boosting Support Vector Machines [PDF]
Revista de Ingeniería, 2006 En este articulo, se presenta un algoritmo de clasificacion binaria basado en Support Vector Machines (Maquinas de Vectores de Soporte) que combinado apropiadamente con tecnicas de Boosting consigue un mejor desempeno en cuanto a tiempo de entrenamiento y conserva caracteristicas similares de generalizacion con un modelo de igual complejidad pero de ...
Elkin García, Fernando Lozano
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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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Coupled Least Squares Support Vector Ensemble Machines
Information, 2019 The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications.
Dickson Keddy Wornyo, Xiang-Jun Shen
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Support Vector Machines for Credit Scoring and discovery of significant features [PDF]
, 2009 The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and
Crook, Jonathan, Bellotti, Tony
core +1 more source