Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …

Automating sign language translation (SLT) is a challenging real-world application. Despite
its societal importance, though, research progress in the field remains rather poor. Crucially …

Asset allocation among diverse financial markets is essential for investors especially under
situations such as the financial crisis of 2008. Portfolio optimization is the most developed …

Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN)
training, with an RNN (the reservoir) being generated randomly, and only a readout being …

Finite mixture model based on the Student's-t distribution, which is heavily tailed and more
robust than Gaussian, has recently received great attention for image segmentation. A new …

Corporate credit rating systems have been an integral part of expert decision making of
financial institutions for the last four decades. They are embedded into the pricing function …

Recently, the hidden Markov model (HMM) with student's t-mixture model (SMM), called
student's t-HMM (SHMM) for short, has received much attention in unsupervised learning of …

Learning a hidden Markov model (HMM) is typically based on the computation of a
likelihood which is intractable due to a summation over all possible combinations of states …

The recent and rapid deployment of CCTV cameras in public areas invokes the need for a
capability to assist human operators in the real-time detection of threats and anomalous …