Since about 100 years ago, to learn the intrinsic structure of data, many representation
learning approaches have been proposed, either linear or nonlinear, either supervised or …

Since Pearson developed principal component analysis (PCA) in 1901, feature learning (or
called representation learning) has been studied for more than 100 years. During this …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

In this paper, two multifault diagnosis methods based on improved support vector machine
(SVM) are proposed for sensor fault detection and identification respectively. First, online …

Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and
presence of mixed pixels are the main challenges in a multi-spectral image classification …

South Asia's coastlines are the most densely inhabited and economically active ecosystems
have already begun to shift due to climate change. Over the past century, climate change …

In this paper, a new multi-class classification framework incorporating geometric data
relationships described in both intrinsic and penalty graphs in multi-class Support Vector …

A common way of solving a multi-class classification problem is to decompose it into a
collection of simpler two-class problems. One major disadvantage is that with such a binary …

Ensemble learning, online learning and deep learning are very effective and versatile in a
wide spectrum of problem domains, such as feature extraction, multi-class classification and …

We assess the accuracy of genocide forecasts made by the Atrocity Forecasting Project
(AFP) for 2011–15, and present new forecasts for 2016–20. Using data from the United …