[PDF] Top 20 High Performance Implementation of Support Vector Machines Using OpenCL
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High Performance Implementation of Support Vector Machines Using OpenCL
... the performance implications from developing highly generalized applications for a traditional, serial envi- ...ronment. OpenCL displays many similarities with CUDA, the parallel programming platform de- ... See full document
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Support vector machines in high-energy physics
... of support vector machines,tools and results, this conclusion includes some advice when and why to use ...of support vectors is therefore ...are using them in physics, new solutions ... See full document
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Multiclass Classification Using Support Vector Machines
... of support vectors it makes and the average prediction time it takes is much less than that of the popular One-Vs-One ...the performance of DCSVM in classifying High Dimension Low Sample Size (HDLSS) ... See full document
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Sparse Deconvolution Using Support Vector Machines
... SD using SVM principles, which we call the AKSM algorithm, has been introduced and ...a high degree of robustness against the presence of non-Gaussian additive ...the support vector ... See full document
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Unsteady aerodynamic modeling at high angles of attack using support vector machines
... This paper presents a pioneer study of using SVMs to model high angle-of-attack unsteady aerodynamics. After a review of SVMs and least squares SVMs (LS-SVMs), an exten- sion of the standard SVMs, a ... See full document
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KernTune: Self-tuning Linux Kernel Performance Using Support Vector Machines
... When the classifier gives us a clear recommendation on which parameter should be adjusted and by how much, we merely have to change the parameter. However, this sim- ple adjustment may cause technical problems: ... See full document
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Using Bag of Concepts to Improve the Performance of Support Vector Machines in Text Categorization
... context vector for word ...sparse, high-dimensional, and ternary, which means that their dimensionality k typically is on the order of thousands and that they consist of a small number of randomly dis- ... See full document
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Support Vector Machines in R
... Keywords: support vector machines, R. 1. Introduction Support Vector learning is based on simple ideas which originated in statistical learning theory (Vapnik ...that Support ... See full document
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Preprocessing Using Support Vector Machines
... “guaranteed spam” and “unclear, resp. unlikely to be spam” before, it is now possible to assess an e-mail as “guaranteed desired”. Therefore, classification becomes more precise and unambiguously. For the remaining ... See full document
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Recognition and Rejection Performance in Wordspotting Systems Using Support Vector Machines
... model, support vector machines, radial basis function kernel, linear kernel 1 Introduction Significant progress has been made in the devel- opment of Automatic Speech Recognition (ASR) technology for ... See full document
6
Support Vector Machines
... Defining the margin as the distance from the hyperplane to the nearest example, the basic observation is that intuitively, we expect a hyperplane with larger margin to generalize better [r] ... See full document
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Support Vector Machines
... algorithms that we’ll see later in this class will also be amenable to this method, which has come to be known as the “kernel trick.” 8 Regularization and the non-separable case The derivation of the SVM as presented so ... See full document
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Support Vector Machines
... side and have f (x i ) < 0 and cases with y i = +1 fall on the other and have f (x i ) > 0. Given that we have achieved that, we could classify new test cases according to the rule y test = sign(x test ). However, ... See full document
5
Support Vector Machines
... VIII. C ONCLUSION In this paper, we introduced a novel framework for building a family of nested support vector machines for the tasks of cost- sensitive classification and density level set ... See full document
13
Support Vector Machines
... The three algorithms, AdaBoost-Stump, SVM-Mid, and SVM-Stump, gen- erate three different kinds of ensembles. AdaBoost-Stump produces finite and sparse ensembles, SVM-Mid produces finite but nonsparse ensembles, and SVM- ... See full document
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Poetry Classification Using Support Vector Machines
... Poetry is a form of literary art in which language is used for its aesthetic and evocative qualities in addition to, or in lieu of, its’ apparent meaning. Classical poetry showed works of art with diverse styles from the ... See full document
6
Support Vector Machines for Characterising Whipple Shield Performance
... output. The net correlation coefficient is assessed as the statistical correlation between matrices of the input attributes and the SVM outputs. Of the 30 input attributes for SVM1, 24 relate to the material properties ... See full document
8
Shallow Semantic Parsing using Support Vector Machines
... The search is constrained in such a way that no two NON -N ULL nodes overlap with each other. To simplify the search, we allowed only N ULL assignments to nodes having a N ULL likelihood above a threshold. While train- ... See full document
8
Label Noise Cleaning Using Support Vector Machines
... as support vectors, so the classifier built with non-support vector examples is relatively noise ...the support vectors can be targeted with this relatively noise free ... See full document
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Estimating Probabilities of Default using Support Vector Machines
... "Support Vector Machines" was used in order to analyse solvent and insolvent German companies and to come up with a separation decision function that would accurately classify new ... See full document
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