Sparse additive machine with ramp loss
Web22. máj 2024 · Sparse additive models [2,3], aiming to deal with the above tasks simulta- neously, have been extensively investigated in the mean regression setting. As a class of models between linear and nonparametric regression, these methods inherit the flexibility from nonparametric regression and the interpretability from linear regression. Web28. jún 2024 · We propose a novel sparse kernel method, named ATk -GSAM, which integrates the average top-k (AT k) loss into group sparse additive models in reproducing …
Sparse additive machine with ramp loss
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WebWe develop a high dimensional nonparametric classification method named sparse additive machine (SAM), which can be viewed as a functional version of support vector machine (SVM) combined with sparse additive modeling. the SAM is related to multiple kernel learning (MKL), but is computationally more efficient and amenable to theoretical analysis. WebA novel ramp loss-based multi-task twin support vector machine with multi-parameter safe acceleration Direct multi-task twin support vector machine (DMTSVM) is an effective algorithm to deal with multi-task classification problems. However, the generated hyperplane may shift to outliers since the hinge loss is used in DMTSVM.
Web1. máj 2016 · They constructed a ramp loss support vector machine (RSVM) by taking the Ramp loss instead of the Hinge loss in the classical SVM, the Ramp loss function limits its maximal loss value distinctly and can put definite restrictions on the influences of outliers so that it is much less sensitive to their presence.
http://proceedings.mlr.press/v22/zhao12/zhao12.pdf Web8. feb 2011 · The ramp loss allows a maximum error of 2 for each training observation, while the hard margin loss calculates error by counting the number of training observations that …
Web28. jún 2024 · Sparse additive models have shown competitive performance for high-dimensional variable selection and prediction due to their representation flexibility and …
WebSparse additive machine with ramp loss. Analysis and Applications, 19(3):509-528, 2024. Yulong Wang, Yuan Yan Tang, Luoqing Li, Hong Chen. Modal regression based atomic … synonyms for armingWebHowever, most of existing methods are formulated under Tikhonov regularization scheme with the hinge loss, which are susceptible to outliers. To circumvent this problem, we propose a sparse additive machine with ramp loss (called ramp-SAM) to tackle classification and variable selection simultaneously. thai temples in singaporeWebSpectral algorithms form a general framework that unifies many regularization schemes in learning theory. In this paper, we propose and analyze a class of thresholded spectral algorithms that are designed based on empirical features. Soft thresholding is adopted to achieve sparse approximations. thai temple seattleWebIn the interest of deriving classifiers that are robust to outlier observations, we present integer programming formulations of Vapnik's support vector machine (SVM) with the ramp loss and hard mar... thai temples in los angeles caWebRamp-KSVCR (Ramp Loss K-Support Vector Classification-Regression); A sparse and robust methodology for multi-class classification problem. This is a guideline about our proposed method named “Ramp Loss K-Support Vector Classification-Regression; Ramp-KSVCR”. In summary, we proposed a precise, sparse and robust approch for multi-class ... synonyms for arghWeb6. máj 2024 · In this paper, a new multi-classification algorithm termed as ramp loss K-nearest neighbor-weighted multi-class twin support vector machine (RKWMTSVM) is proposed by replacing the hinge loss with ramp loss function in KWMTSVM. This modification leads to a precise, sparse and robust algorithm with better performance. thai temple san brunoWebmonly used to recover sparse solutions in the case of sim-ple dictionaries that consist of one-dimensional spaces H j(see, e.g, Koltchinskii (2007) and references therein). The goal is to extend this methodology to more general class of problems that include aggregation of large en-sembles of kernel machines and sparse additive models. synonyms for armageddon