Distributed machine learning (ML) was originally introduced to solve a complex ML problem
in a parallel way for more efficient usage of computation resources. In recent years, such …

Support Vector Machines (SVM) are widely used as supervised learning models to solve the
classification problem in machine learning. Training SVMs for large datasets is an extremely …

This paper explores the use of Tensor Engines to accelerate nonlinear and linear SVM
training. Support Vector Machine (SVM) is a classical machine learning model for …

Kernel Support Vector Machine (SVM) is a popular machine learning model for classification
and regression. A significant challenge of large scale Kernel SVM is the size of the Gram …

Nowadays, data is being generated very rapidly worldwide, and we need to analyze this
data distributedly and efficiently. In this paper, we provide a method named SVDSVM that …

Support vector machine (SVM) is a fundamental machine learning method with solid
mathematical theory and high effectiveness in many applications. Because distributed …

Many real-world application problems can be modeled mathematically as constrained
convex optimization problems. The scale of such problems can be extremely large, posing …

The emergence of neural engines such as Nvidia TensorCore GPU brings a revolution to
deep neural networks, as the neural engines can perform extremely fast general matrix …

In the era of big data and IoT, devices at the edges are becoming increasingly intelligent,
and processing the data closest to the sources is paramount. However, conventional …

Support Vector Machine (SVM) is a widely used supervised machine learning algorithm for
classification. Training SVM is challenging due to high computational cost and memory …