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Zhang, P., Peng, J.: SVM vs regularized least squares classification. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 1, pp. 176-179 (2004)P. Zhang, J. Peng, SVM vs regularized least squares classification, in: Proceedings of the 17th International ...
如SMO,Kernel的方法。 可是这里要提到的Regularized least-squaresclassification是一个和他有着相同的效果的分类器。比較而言计算却比較的简单(We see that a Regularized Least-Squares Classification problem can be solved by solving a single system of linear equations.)。接下来将对其进行介绍。 首先我们知道终于...
In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the so
Shao Y, Wang Z, Chen W, Deng N (2013) Least squares twin parametric-margin support vector machine for classification. Appl Intell 39:451–464Shao YH, Wang Z, Chen WJ, Deng NY (2013) Least squares twin parametric-margin support vector machines for classification. Appl Intell 39(...
This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called $varepsilon$-dragging is introduced ...
Least squares support vector machine classifiers: a large scale algorithm Support vector machines (SVM's) have been introduced in literature as a method for pattern recognition and function estimation, within the framework of sta... JAK Suykens,L Lukas,P Van,... 被引量: 1.1万发表: 1999年 Ba...
An Orthogonal Least Squares (OLS) based feature selection method is proposed for both binomial and multinomial classification. The novel Squared Orthogonal Correlation Coefficient (SOCC) is defined based on Error Reduction Ratio (ERR) in OLS and used as the feature ranking criterion. The equivalence ...
LEAST SQUARES UNIVERSUM TSVM Supervised learning problem with Universum data is a new research subject in machine learning. Universum data, which are not belonging to any class of the classification problem of interest, has been proved very helpful in learning. For ... L Fan 被引量: 0发表: ...
Adaptive Semi-Supervised Learning with Discriminative Least Squares Regression Instead of using traditional least squares regression (LSR) for classification, we develop a new discriminative LSR by equipping each label with an adjustment vector. This strategy avoids incorrect penalization on samples that are...