Abstract. Tuning hyper-parameters is a necessary step to improve learning algorithm performances. For Support Vector Machine classifiers, adjusting kernel parameters increases dra...
Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are ...
This paper presents a novel dimension reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-c...
Senjian An, Wanquan Liu, Svetha Venkatesh, Ronny T...
In this study, a support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose...
Abstract A new approach to algorithmic trading system development is presented. This approach, Kernel Price Pattern Trading (KPPTP ), allows the practitioner to link the performanc...