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 …

This book has been drafted based on my lectures and seminars from recent years for
postgraduate students at Auckland University of Technology (AUT), New Zealand. We have …

Sign language recognition is one of the fundamental ways to assist deaf people to
communicate with others. An accurate vision-based sign language recognition system using …

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials
and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage …

Complex activity recognition is challenging due to the inherent uncertainty and diversity of
performing a complex activity. Normally, each instance of a complex activity has its own …

Feature selection is the process of reducing the number of collected features to a relevant
subset of features and is often used to combat the curse of dimensionality. This paper …

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 …

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 …

In this paper, a joint feature selection and parameter estimation algorithm is presented for
hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs). New …

In this paper, we propose a Bayesian inference method for the generalized Gamma mixture
model (GΓMM) based on variational expectation-maximization algorithm. Specifically, the …