نتایج جستجو برای: variably scaled radial kernel

تعداد نتایج: 133573  

2014
Hetal Bhavsar Amit Ganatra

Support Vector Machine (SVM) is one of the most robust and accurate method amongst all the supervised machine learning techniques. Still, the performance of SVM is greatly influenced by the selection of kernel function. This research analyses the characteristics of the two well known existing kernel functions, local Gaussian Radial Basis Function and global Polynomial kernel function. Based on ...

2016
Alexander Askinadze

Um als Bildungsanbieter bei gefährdeten Studenten rechtszeitig intervenierend eingreifen zu können, sind Verfahren zur Vorhersage studentischer Leistungen notwendig. Viele Arbeiten haben den Einsatz des SVM-Klassifikators vorgeschlagen. Allerdings wurden unzureichende Angaben zur Wahl eines geeigneten Kernel gegeben. Außerdem kann der SVM-Klassifikator bei fehlenden Trainingsdaten zu allen mögl...

Journal: :Journal of the Korean Chemical Society 2012

Journal: :J. Complexity 2010
Clint Scovel Don R. Hush Ingo Steinwart James Theiler

We describe how to use Schoenberg’s theorem for a radial kernel combined with existing bounds on the approximation error functions for Gaussian kernels to obtain a bound on the approximation error function for the radial kernel. The result is applied to the exponential kernel and Student’s kernel. To establish these results we develop a general theory regarding mixtures of kernels. We analyze t...

2016
Lukasz Struski Marek 'Smieja Jacek Tabor

We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based...

Journal: :J. Multivariate Analysis 2013
Lo-Bin Chang Zhidong Bai Su-Yun Huang Chii-Ruey Hwang

Many kernel-based learning algorithms have the computational load scaled with the sample size n due to the column size of a full kernel Gram matrix K. This article considers the Nyström low-rank approximation. It uses a reduced kernel ?̂?, which is n×m, consisting of m columns (say columns i1, i2,···, im) randomly drawn from K. This approximation takes the form K ≈ ?̂?U?̂?, where U is the reduced ...

2014
Fenghua Huang Luming Yan

To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1) different radial basis kernel functions (RBFs) are employed for spectral and textural features, and a new combined radial...

2002
Yassine Ben Ayed Dominique Fohr Jean Paul Haton Gérard Chollet

Support Vector Machines (SVM) is one such machine learning technique that learns the decision surface through a process of discrimination and has a good generalization capacity [6]. SVMs have been proven to be successful classifiers on several classical pattern recogntion problems [9, 11]. In this paper, one of the first applications of Support Vector Machines (SVM) technique for the problem of...

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