Examples of using Support vector in English and their translations into Portuguese
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Support vector machine.
RBFs are also used as a kernel in support vector classification.
Application of support vector machines and autoregressive moving average models in….
Moreover, one class classifiers and support vector machines are evaluated.
Support vector machines, however, do not consider the temporal aspects of data, characteristics that are important for gesture analysis. therefore, this paper inve.
While at AT&T,Vapnik and his colleagues developed the theory of the support vector machine.
Neural networks and support vector machines as diagnosing tool for industrial level….
Assessment of a method based on multiple kernel support vector machines and images….
The detection is based on support vector machines(svm) with gaussian kernel or the inner product detector ipd.
The following five classification meth- ods are investigated: support vector machines, k-nearest neighbor.
Version 2016.3 adds support vector machine models and a free version that activates when the trial version is over.
She has worked with classification systems like decision trees, support vector machines, and more.
Version 2016.3 adds support vector machine models, and a free version that activates automatically after the free trial.
The techniques used for these purposes are aakr(autoassociative kernel regression)and svdd support vector data description.
The supervised classifier support vector machine(svm), has been used in different applications due to its good performance.
In this dissertation we investigate andtest a methodology to optimize the kernel parameters in a support vector machines classifier.
In chapter 2,different support vector machines were built for eucalyptus production prognosis, and these results were compared to those by growth model and artificial neural networks.
In this work, we propose a novel methodology for geometry reconstruction in cad models using support vector machines and shape descriptors.
Furthermore, we evaluated the use of svm(support vector machines) to estimate the user profile, comparing this approach with a simpler one.
At first, we present and use three text classification techniques, known as, naive-bayes,decision trees and support vector machines svm.
We show that support vector regression models satisfy the assumptions required for the global convergence of the trust-region algorithm.
Abstract one of the most prominent ways of image classi cation nowadays is describing them with image content descriptors and use a support vector machine(svm) classi er.
The disease? risk is evaluated by classifying patient? s data with a support vector machine model, thenmeasuring the Euclidian distance to the hyperplane decision function.
Support Vector Machines(SVM) belong to a family of supervised learning algorithms that can efficiently solve classification and regression problems.
An on-line handwriting recognition engine based upon statistical dynamic time warping(SDTW) and support vector machines with a Gaussian DTW kernel SVM-GDTW.
Support vector machines were selected as classification method, because of its ability to generalize and good results obtained for many complex problems.
The purpose of this study is to evaluate the performance of an artificial intelligence technique, support vector machines(svm), on projecting the production of eucalyptus stands.
We feel that Support Vector Machines have helped us to reach a quality and speed of classification that we could have never achieved with a non-machine learning approach.
This dissertation compares three cascade multitemporal image classification methods based on:(1) support vector machines(svm),(2) hidden markov models(hmm) and(3) fuzzy markov chains fmc.
Support vector machines(SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression.