支持向量机和支持向量回归是目前机器学习领域用得较多的方法,不管是人脸识别,字符识别,行为识别,姿态识别等,都可以看到它们的影子。在我的工作中,经常用到支持向量机和支持向量回归,然而,作为基本的理论,却没有认真地去梳理和总结,导致有些知识点没有彻底的弄明白。这篇博客主要就是想梳理一遍支持向量机和支持向量回...
One of the advantages ofSupport Vector Machine, andSupport Vector Regressionas the part of it, is that it can be used to avoid difficulties of using linear functions in the high dimensional feature space and optimization problem is transformed into dual convex quadratic programmes. In regression ca...
Electromagnetic parametersMagneticSupport vector machineA new method is proposed for electromagnetic parameters (permittivity and permeability) measurement. The microstrip transmission-line is used as measure structure, and supported vector machine (SVM) is introduced to extract actual permittivity and ...
Support vector machines for regression models For greater accuracy on low- through medium-dimensional data sets, train a support vector machine (SVM) model usingfitrsvm. For reduced computation time on high-dimensional data sets, efficiently train a linear regression model, such as a linear SVM mo...
Refer to API Reference: Support Vector Machine Classifier and Regression. Examples oneAPI DPC++ Batch Processing: dpc_svm_two_class_thunder_dense_batch.cpp oneAPI C++ Batch Processing: cpp_svm_two_class_smo_dense_batch.cpp cpp_svm_two_class_thunder_dense_batch.cpp cpp_s...
using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Compone...
Support vector regression (SVR) Support vector regression has been developed based on support vectors introduced by Vapnik [11]. Support vector machine (SVM) is a well-known method for classification which has been also used in gene selection field [12]. Such as SVM, SVR has been applied in...
This paper deals with interval regression analysis using support vector machine and quantile regression method. The algorithm consists of two phases - the identification of the main trend of the data and the interval regression based on acquired main trend. Using the principle of support vector machi...
Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992[5]. SVM regression is considered a nonparametric technique because it relies on kernel functions. ...
Most existing online algorithms in support vector machines (SVM) can only grow support vectors. This paper proposes an online error tolerance based support vector machine (ET-SVM) which not only grows but also prunes support vectors. Similar to least square support vector machines (LS-SVM), ET...