While detecting and interpreting temporal patterns of nonverbal behavioural cues in a given
context is a natural and often unconscious process for humans, it remains a rather difficult …

When used for structured regression, powerful Conditional Random Fields (CRFs) are
typically restricted to modeling effects of interactions among examples in local …

This authoritative work provides an in-depth treatment of state space methods, with a range
of applications in neural and clinical data. Advanced and state-of-the-art research topics are …

Fusing multiple continuous expert annotations is a crucial problem in machine learning and
computer vision, particularly when dealing with uncertain and subjective tasks related to …

We consider the task of labeling facial emotion intensities in videos, where the emotion
intensities to be predicted have ordinal scales (eg, low, medium, and high) that change in …

Due to the increase in extreme weather conditions and aging infrastructure deterioration, the
number and frequency of electricity network outages is dramatically escalating, mainly due …

Abstract We propose a Conditional Random Field (CRF) model for structured regression. By
constraining the feature functions as quadratic functions of outputs, the model can be …

In this work a class of hybrid morphological perceptrons, called dilation–erosion perceptron
(DEP), is presented to overcome the random walk dilemma in the time series forecasting …

The reinforce leaning control algorithm is studied in this paper. Two algorithms are designed
using one-stage inverted pendulum as an object. One is Q-learning control algorithm, the …

Several linear and non-linear techniques have been proposed to solve the stock market
forecasting problem. However, a limitation arises from all these techniques and is known as …