Support vector machine (SVM) is an optimal margin based classification technique in
machine learning. SVM is a binary linear classifier which has been extended to non-linear …

We propose a multimodal, decomposable model for articulated human pose estimation in
monocular images. A typical approach to this problem is to use a linear structured model …

Current research endeavors in the application of artificial intelligence (AI) methods in the
diagnosis of the COVID-19 disease has proven indispensable with very promising results …

In many problems of machine learning, the data are distributed nonlinearly. One way to
address this kind of data is training a nonlinear classifier such as kernel support vector …

Our objective is to speed up non-linear SVM prediction while maintaining classification
accuracy above an acceptable limit. We generalize Localized Multiple Kernel Learning so …

In last decade lot of efforts had been made by research community to create sign language
recognition system which provide a medium of communication for differently-abled people …

Abstract Support Vector Machine (SVM) stands out as a prominent machine learning
technique widely applied in practical pattern recognition tasks. It achieves binary …

Graph neural networks (GNNs) emerge as a powerful family of representation learning
models on graphs. To derive node representations, they utilize a global model that …

Abstract We propose the Anchored Regression Network (ARN), a nonlinear regression
network which can be seamlessly integrated into various networks or can be used stand …

Most state-of-the-art approaches to image segmentation formulate the problem using
Conditional Random Fields. These models typically include a unary term and a pairwise …