Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

Abstract: In this work, machine learning methods are applied to high-speed channel modeling for signal integrity analysis. Linear, support vector, and deep neural network (DNN) regressions are adopted ...

Abstract: The M-ary support vector machine (SVM) is introduced as a nonparameter nonlinear phase noise (NLPN) mitigation approach for the coherent optical systems. The NLPN tolerance of the system can ...

Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can ...

Inertial microfluidics allows for passive, label-free manipulation of particles suspended in a fluid. Physical experiments can understand the underlying mechanisms to an extent whereby inertial ...

Dataset import and preprocessing Automatic feature map generation 27-type feature catalogue for iterative (re)calculation to support model integration into optimization 7 customizable internal model ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

LONDON, ENGLAND - AUGUST 19: Workers clean the East-facing clock face of the Elizabeth Tower of the Houses of Parliament on August 19, 2014 in London, England. Workers are cleaning the four faces of ...