Kernel methods and deep learning are two of the most currently remarkable machine
learning techniques that have achieved great success in many applications. Kernel methods …

In clinical medicine, multidimensional time series data can be used to find the rules of
disease progress by data mining technology, such as classification and prediction. However …

In this paper, a novel feature line embedding (FLE) algorithm based on support vector
machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for …

Multiple kernel learning (MKL) is a principled approach to kernel combination and selection
for a variety of learning tasks, such as classification, clustering, and dimensionality …

Convex optimization techniques are extensively applied to various models, algorithms, and
applications of machine learning and data mining. For optimization-based classification …

For one given scene, multimodal data are acquired from multiple sensors. They share some
similarities across the sensor types (redundant part of the information, also called coupling …

As a highly active topic in computational paralinguistics, speech emotion recognition (SER)
aims to explore ideal representations for emotional factors in speech. In order to improve the …

Abstract Multiple Empirical Kernel Learning (MEKL) explicitly maps samples into different
empirical feature spaces in which the kernel features of the mapped samples can be directly …

The recovery of the intrinsic geometric structures of data collections is an important problem
in data analysis. Supervised extensions of several manifold learning approaches have been …

Heart Failure (HF) is one of the most common causes of hospitalization and is burdened by
short-term (in-hospital) and long-term (6–12 month) mortality. Accurate prediction of HF …