This paper describes our approach towards robust facial expression recognition (FER) for
the third Emotion Recognition in the Wild (EmotiW2015) challenge. We train multiple deep …

Diversity among the members of a team of classifiers is deemed to be a key issue in
classifier combination. However, measuring diversity is not straightforward because there is …

Geographical location is vital to geospatial applications like local search and event
detection. In this paper, we investigate and improve on the task of text-based geolocation …

The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

Support vector machines (SVMs) have considerable potential as classifiers of remotely
sensed data. A constraint on their application in remote sensing has been their binary …

Currently, ensemble approaches based, among other things, on the use of non-network
models are powerful tools for solving data analysis problems in various practical …

Dynamic ensemble selection systems work by estimating the level of competence of each
classifier from a pool of classifiers. Only the most competent ones are selected to classify a …

Defect detection using machine vision technology plays an important role in the
manufacturing process of mobile phone screen glass (MPSG). This study proposes an …

Metamaterials are attracting increasing interest in the field of acoustics due to their sound
insulation effects. By periodically arranged structures, acoustic metamaterials can influence …

Individual classification models are recently challenged by combined pattern recognition
systems, which often show better performance. In such systems the optimal set of classifiers …