منابع مشابه
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the mini...
متن کاملWeighted Twin Support Vector Machine with Universum
Universum is a new concept proposed recently, which is defined to be the sample that does not belong to any classes concerned. Support Vector Machine with Universum (U-SVM) is a new algorithm, which can exploit Universum samples to improve the classification performance of SVM. In fact, samples in the different positions have different effects on the bound function. Then, we propose a weighted ...
متن کاملRobustified distance based fuzzy membership function for support vector machine classification
Fuzzification of support vector machine has been utilized to deal with outlier and noise problem. This importance is achieved, by the means of fuzzy membership function, which is generally built based on the distance of the points to the class centroid. The focus of this research is twofold. Firstly, by taking the advantage of robust statistics in the fuzzy SVM, more emphasis on reducing the im...
متن کاملA Weighted Support Vector Machine for Data Classification
This paper presents a weighted support vector machine (WSVM) to improve the outlier sensitivity problem of standard support vector machine (SVM) for two-class data classification. The basic idea is to assign different weights to different data points such that the WSVM training algorithm learns the decision surface according to the relative importance of data points in the training data set. Th...
متن کاملA Weighted Least Squares Twin Support Vector Machine
Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM. However, same penalties are given to the negative samples when constructing the hyper-plane for...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics and Its Interface
سال: 2015
ISSN: 1938-7989,1938-7997
DOI: 10.4310/sii.2015.v8.n3.a7