With the development of Raman spectroscopy and the expansion of its application domains,
conventional methods for spectral data analysis have manifested many limitations. Exploring …

In cognitive science and neuroscience, there have been two leading models describing how
humans perceive and classify facial expressions of emotion—the continuous and the …

Providing a broad but in-depth introduction to neural network and machine learning in a
statistical framework, this book provides a single, comprehensive resource for study and …

Linear discriminant analysis (LDA) is one of the most important supervised linear
dimensional reduction techniques which seeks to learn low-dimensional representation from …

To recognize speech, handwriting, or sign language, many hybrid approaches have been
proposed that combine dynamic time war** (DTW) or hidden Markov models (HMMs) with …

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis
(MMRA) for exclusively dealing with the long-standing “worst-case class separation” …

We propose a new criterion for discriminative dimension reduction, max-min distance
analysis (MMDA). Given a data set with C classes, represented by homoscedastic …

Kernel map** is one of the most used approaches to intrinsically derive nonlinear
classifiers. The idea is to use a kernel function which maps the original nonlinearly …

Degraded tropical forests dominate agricultural frontiers and their management is becoming
an urgent priority. This calls for a better understanding of the different forest cover states and …

By combining different facial muscle actions, called action units, humans can produce an
extraordinarily large number of facial expressions. Computational models and studies in …