Examples of using Vector machine in English and their translations into Portuguese
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Support vector machine.
While at AT&T, Vapnik andhis colleagues developed the theory of the support vector machine.
The next evolution was called Vector machine or machine SIMD.
The new objects will be divided into your predefined classes with a support vector machine.
The model generated by the support vector machine SVM showed AUC of 0.991 sensitivity 96%, specificity 98.
The techniques were used in conjunction with dimensionality reduction through principal component analysis(pca)and a support vector machine(svm) classifier.
The system is a support vector machine model trained with three measurements of hydrophobic moment with accuracy of 83.5.
The data extracted from the model was used to train a svm(support vector machine) classification algorithm.
Version 2016.3 adds support vector machine models and a free version that activates when the trial version is over.
PLS is widely used for identifying biomarkers and in classifying diseases,while support vector machine(SVM) is used in cancer research. Pathway Analysis Methods.
Version 2016.3 adds support vector machine models, and a free version that activates automatically after the free trial.
Abstract one of the most prominent ways of image classi cation nowadays is describing them withimage content descriptors and use a support vector machine(svm) classi er.
The method of artificial intelligence, or support vector machine SVM, is a widely used method for the recognition of patterns, such as face recognition, handwritten character recognition.
Nevertheless, pnns presented promising results when compared to other state-of-the-art algorithms for the same purposes,such as convolutional neural network(cnn) and support vector machine svm.
This process uses a combination of classifiers, with c4.5 decision tree,naive bayes and support vector machine, constructed by samples of the data set with automobile claims.
In this work, we studied six different methods applied to supervised classification problems(when there is a known response for the model training): logistic regression, decision tree, naive bayes, knn(k-nearest neighbors),neural networks and support vector machine.
To evaluate the proposed method,the 10-fold cross validation technique was applied using the classi ers support vector machine(svm), random forest(raf) and radial basis function rbf.
The channel estimation part is based on fast support vector machine(svm) with adaptive update of coefficients, allowing its training in real time and its application in time-varying channels.
This work aims to evaluate the efficiency of using meta-heuristic strategies in the selection and tuning of these parameters, when support vector machine is applied to brain-computer interface.
The obtained results with this approach,using support vector machine with genetic algorithm, show better results than a pure iterative compilation using a genetic algorithm, reaching speedups of 2,115x over program without optimizations, while a pure iterative compilation reached 2,074x.
Classification algorithms covered in this course include nearest neighbor algorithm, support vector machine(SVM) algorithm, Bayesian methods, decision trees, lists of rules.
For this purpose, we use independent components analysis to extract characteristics of these signals, algorithm of maximum relevance and minimum redundancy to reduce the number of features andcomputational costs and support vector machine to qualify them.
One of the most widely used data classification algorithms in the literature is the support vector machine which, despite possessing positive features in its application, is sensitive to control parameters.
We will verify the consequences of ranking techniques in subspace learning through fisher criterion, by estimating the covariance structure of the database and by using weights generated through separating hyperplanes,such as support vector machine(svm) and linear discriminant analysis lda.
Open Access Editor's comment: This study comprehensively evaluated sequence-,motif- and SVM(Support Vector Machine)-based computational prediction approaches for allergens and optimized their parameters to obtain better performance.
Thus, the method is based on semivariogram, semimadogram, covariogram and correlogram functions, used as representative characteristics for the samples,which will be classified as fault or"non fault" regions by the pattern recognition technique named support vector machine svm.
Apart from that, this thesis focuses on an empirical data analysis of a financial institution,as well as the application of support vector machine and decision tree algorithms; bagging, adaboost and random forest.
This study aimed to:1 evaluate the performance of the maximum likelihood classifiers and support vector machine and the contribution of different compositions of multispectral bands, the vegetation index(ndvi), and principal component analysis of sensor images tm/landsat5 for separating features of muçunungas.
The classifiers used were maximum likelihood, mahalanobis distance, minimum distance, parallelepiped, spectral angle mapper(sam),support vector machine(svm) and spectral information divergence sid.
The tested rating models were: support vector machine(svm) and independent flexible modeling by analogy class(simca); and quantification: regression by partial least square(pls) by interval(ipls), principal component regression(pcr), regression by support vectors machine(svr) and artificial neural networks rna.