Sentence examples for multiple support vectors from inspiring English sources

This is typical in many classifiers such as the K-NN [9], which uses different prototypes for each class, and the SVM [10], which uses multiple support vectors.

A multiple support vector machine (SVM) based mid-term electricity MCP forecasting model is proposed in this paper.

This paper studies how joint training of multiple support vector machines (SVMs) can improve the effectiveness and efficiency of automatic image annotation.

Among many supervised learning methods, support vector machine attracts much attention for binary classification problems and its extension, namely multiple support vector machines, is able to solve multiclass classification problems.

We utilized a cascaded approach to train multiple support vector machine (SVM) classifiers using combinations of feature subtypes to enable the possibility of maximizing the performance by leveraging different feature sets extracted from multiple levels.

We propose a feature selection method based on multiple support vector machine recursive feature elimination (MSVM-RFE).

In our preliminary selection, all genes in L. interrogans strain #56601 were searched using P-CLASSIFIER, a system for predicting the subcellular locations of proteins on the basis of amino acid subalphabets and a combination of multiple support vector machines[ 33].

Data classification was carried out with a multiple class support vector machine tool (mcSVM) [ 49, 50].

In this study, a data-driven based 3D FLC design method using multiple single-output support vector regressions (SVRs) is proposed for SDDSs with multiple control sources.

Three regression algorithms have been conducted which are multiple linear regression, support vector regression and decision tree regression to evaluate their effectiveness of making forecasts.

In [10], classification of both EEG mental and cognitive tasks is reported based on Wavelet packet entropy and Granger causality, where classification performance is evaluated using multiple kernel learning support vector machine (SVM) classifier.