Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

The prediction of global solar radiation for the regions is of great importance in terms of
giving directions of solar energy conversion systems (design, modeling, and operation) …

Pretrained language models (LMs) perform well on many tasks even when learning from a
few examples, but prior work uses many held-out examples to tune various aspects of …

DNA micro-arrays now permit scientists to screen thousands of genes simultaneously and
determine whether those genes are active, hyperactive or silent in normal or cancerous …

In this work, we propose hyperparameters optimization using grid search to optimize the
parameters of eight existing models and apply the best parameters to predict the outcomes …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

This paper addresses the problem of the classification of hyperspectral remote sensing
images by support vector machines (SVMs). First, we propose a theoretical discussion and …

Abstract Model selection strategies for machine learning algorithms typically involve the
numerical optimisation of an appropriate model selection criterion, often based on an …

Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

Human action recognition is an important yet challenging task. The recently developed
commodity depth sensors open up new possibilities of dealing with this problem but also …