نتایج جستجو برای: least squares support vector machine
تعداد نتایج: 1376312 فیلتر نتایج به سال:
Résumé. Les algorithmes de boosting de Newton Support Vector Machine (NSVM), Proximal Support Vector Machine (PSVM) et Least-Squares Support Vector Machine (LS-SVM) que nous présentons visent à la classification de très grands ensembles de données sur des machines standard. Nous présentons une extension des algorithmes de NSVM, PSVM et LS-SVM, pour construire des algorithmes de boosting. A cett...
In this paper, a modified least squares support vector machine classifier, called the C-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importan...
In this work, we developed classifiers to distinguish between four ovarian tumor types using Bayesian least squares support vector machines (LS-SVMs) and kernel logistic regression. Input selection using rank-one updates for LS-SVMs performed better than automatic relevance determination. Evaluation on an independent test set showed good performance of the classifiers to distinguish between all...
Least Squares Support Vector Machines (LS-SVMs) were proposed by replacing the inequality constraints inherent to L1-SVMs with equality constraints. So far this idea has only been suggested for a least squares (L2) loss. We describe how this can also be done for the sumof-slacks (L1) loss, yielding a new classifier (Least 1-Norm SVMs) which gives similar models in terms of complexity and accura...
The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsp...
In order to diagnose incipient fault of analog circuits effectively, an analog circuit incipient fault approach by using kernel entropy component analysis (KECA) as a preprocessor is proposed in the paper. Time responses are acquired by sampling outputs of the circuits under test. Raw features with high dimension are generated by wavelet transform. Furthermore, lower dimensional features are pr...
In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require t...
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