Very sparse LSSVM reductions for large-scale data
R Mall, JAK Suykens - IEEE transactions on neural networks …, 2015 - ieeexplore.ieee.org
Least squares support vector machines (LSSVMs) have been widely applied for
classification and regression with comparable performance with SVMs. The LSSVM model …
classification and regression with comparable performance with SVMs. The LSSVM model …
Learning imbalanced classifiers locally and globally with one-side probability machine
We consider the imbalanced learning problem, where the data associated with one class
are far fewer than those associated with the other class. Current imbalanced learning …
are far fewer than those associated with the other class. Current imbalanced learning …
Sparse least squares support vector machine with L0-norm in primal space
Q Li, X Li, W Ba - 2015 IEEE International Conference on …, 2015 - ieeexplore.ieee.org
Least squares support vector machine (LS-SVM) has been successfully applied in many
classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack …
classification and regression tasks. The main drawback of the LS-SVM algorithm is the lack …
Two-layer mixture of factor analyzers with joint factor loading
Dimensionality Reduction (DR) is a fundamental yet active research topic in pattern
recognition and machine learning. When used in classification, previous research usually …
recognition and machine learning. When used in classification, previous research usually …
[PDF][PDF] Improved Iterative Sparseness for Least Squares Support Vector Machines
LS Zhong, LY Chen, JH Gong… - Applied Mathematics & …, 2015 - naturalspublishing.com
An improved iterative sparse algorithm is proposed to accelerate the execution of sparse
least squares support vector machines (LS-SVM). Firstly, the technique of iterative …
least squares support vector machines (LS-SVM). Firstly, the technique of iterative …