Results 281 to 290 of about 937,595 (329)
Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine. [PDF]
Sci RepTahir M +5 more
europepmc +1 more source
A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine. [PDF]
BMC CancerLuo Y +12 more
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, 2011 Supervised regression/classification methods learn a model of relation between the target vectors \(\{y_i \}_{i=1}^N\) and the corresponding input vectors \(\{{\mathbf {x}}_i\}_{i=1}^N\) consisting of N training samples and utilize this model to predict/classify target values for the previously unseen inputs.
M. Narasimha Murty, V. Susheela Devi
+12 more sources
IEEE Intelligent Systems and their Applications, 1998 My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable.
Hearst, M. +4 more
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2019
In this chapter we are going to study the concept of support vector machines as developed by Vapnik and others. This concept was first proposed as an alternative to neural networks, when neural networks were not performing up to the grand expectations that they came with.
Brandon Greenwell, Bradley C. Boehmke
+9 more sources
In this chapter we are going to study the concept of support vector machines as developed by Vapnik and others. This concept was first proposed as an alternative to neural networks, when neural networks were not performing up to the grand expectations that they came with.
Brandon Greenwell, Bradley C. Boehmke
+9 more sources
2013
In this chapter, we discuss the support vector machine (SVM), an approach for classification that was developed in the computer science community in the 1990s and that has grown in popularity since then. SVMs have been shown to perform well in a variety of settings, and are often considered one of the best “out of the box” classifiers.
Daniela Witten +3 more
+9 more sources
In this chapter, we discuss the support vector machine (SVM), an approach for classification that was developed in the computer science community in the 1990s and that has grown in popularity since then. SVMs have been shown to perform well in a variety of settings, and are often considered one of the best “out of the box” classifiers.
Daniela Witten +3 more
+9 more sources
Fuzzy support vector machines [PDF]
IEEE Transactions on Neural Networks, 2002 A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
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2015
In this chapter, we study support vector machines (SVM). We will see that optimization methodology plays an important role in building and training of SVM.
N. Cristianini, RICCI, ELISA
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In this chapter, we study support vector machines (SVM). We will see that optimization methodology plays an important role in building and training of SVM.
N. Cristianini, RICCI, ELISA
openaire +3 more sources
2020
Support vector machine is a method for classification and regression that draws an optimal boundary in the space of covariates (p dimension) when the samples \((x_1, y_1), \ldots , (x_N, y_N)\) are given. This is a method to maximize the minimum value over \(i = 1, \ldots , N\) of the distance between \(x_i\) and the boundary.
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Support vector machine is a method for classification and regression that draws an optimal boundary in the space of covariates (p dimension) when the samples \((x_1, y_1), \ldots , (x_N, y_N)\) are given. This is a method to maximize the minimum value over \(i = 1, \ldots , N\) of the distance between \(x_i\) and the boundary.
openaire +4 more sources
2005
The availability of reliable learning systems is of strategic importance, as many tasks cannot be solved by classical programming techniques, because no mathematical model of the problem is available. So, for example, no one knows how to write a computer program that performs handwritten character recognition, though plenty of examples are available ...
Mamoun Awad, Latifur Khan
openaire +1 more source
The availability of reliable learning systems is of strategic importance, as many tasks cannot be solved by classical programming techniques, because no mathematical model of the problem is available. So, for example, no one knows how to write a computer program that performs handwritten character recognition, though plenty of examples are available ...
Mamoun Awad, Latifur Khan
openaire +1 more source