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Machine-mediated learning, 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.
D. Pisner, David M Schnyer
semanticscholar +5 more sources
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.
D. Pisner, David M Schnyer
semanticscholar +5 more sources
, 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.
David M. J. Tax +6 more
+8 more sources
Distance-based support vector machine to predict DNA N6-methyladenine modification
Current Bioinformatics, 2022DNA N6-methyladenine plays an important role in the restriction-modification system to isolate invasion from adventive DNA. The shortcomings of the high time-consumption and high costs of experimental methods have been exposed, and some computational ...
Haoyu Zhang +4 more
semanticscholar +1 more source
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
openaire +2 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
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
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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Support Vector Machine Accuracy Improvement with Classification
International Conference on Computational Intelligence and Communication Networks, 2020Rapid increase in the information technology through digitalization, leads to fast enhancement in technical industry has expanded the need for effective data mining.
L. Mohan +3 more
semanticscholar +1 more source