In recent years, the rapid development of sensors and information technology has made it
possible for machines to recognize and analyze human emotions. Emotion recognition is an …

With the technology development in cyber physical systems and big data, there are huge
potential to apply them to achieve personalization and improve resource efficiency in …

For spline regressions, it is well known that the choice of knots is crucial for the performance
of the estimator. As a general learning framework covering the smoothing splines, learning …

The goal of learning theory is to approximate a function from sample values. To attain this
goal learning theory draws on a variety of diverse subjects, specifically statistics …

We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …

In conditionally automated driving, drivers have difficulty taking over control when requested.
To address this challenge, we aimed to predict drivers' takeover performance before the …

Many works related learning from examples to regularization techniques for inverse
problems, emphasizing the strong algorithmic and conceptual analogy of certain learning …

CCS Concepts:• Computing methodologies→ Machine learning approaches; Machine
learning;• Social and professional topics→ Computing/technology policy; Medical …

This paper considers the regularized learning algorithm associated with the least-square
loss and reproducing kernel Hilbert spaces. The target is the error analysis for the …

Support vector machine (SVM) soft margin classifiers are important learning algorithms for
classification problems. They can be stated as convex optimization problems and are …