… Principal component analysis is used to improve the model's efficiency for effective feature
selection and dimensionality reduction. This study presents a novel framework incorporating …

… to elevate the effectiveness of the SVM classifier, similar to that … Multi-kernel SVM was an
improved SVM architecture in which … -linear principal component analysis (MPCA) with SVM for …

… The single kernel based classifier gives poor performance to classify the complex data.
Further, the hidden information cannot be retrieved by global feature vectors which only provide …

… release, spectral analysis, principal component analysis as well as wavelet alteration…
Efficiency of the classification This section describes the effectiveness of medical data classification …

… The proposed method uses different kernels for SVM, and different kernel performances are
… classify the arrhythmias, multi-kernel support vector machine used for the classification of …

… implemented by SVM, which means the target values of \({y}_{i}\) are the linear weighted …
superior performance of multi-kernel in capturing the global structure of the nonlinear dataset. …

… In addition, in order to verify the superior performance of the optimized MK-RVM compared
with the existing proven method, the multi-kernel support vector machine (MK-SVM) was …

… them to probabilistic principal component analysis (PPCA) for … We hypothesized that the
performance of the classifier would … we used multi-kernel-SVM to resolve the non-linear difficulty …

… SVM is a linear classification model, its algorithm goal is to find … the impact of different s and
k on classification performance in … mity of the experiment, all the PCA operations involved in …

… on diabetes-related disorders, using support vector machine (SVM) and random forest (RF)
machine learning methods. Principal component analysis (PCA) was utilized to decrease the …