In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly impor...
This paper adresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-...
This paper presents an approach to computer-assisted teaching of reading abilities using corpus data. The approach is supported by a set of tools for automatically selecting and c...
: In this study, we introduce a set of one-dimensional features to represent two dimensional shape information for HMM (Hidden Markov Model) based handwritten optical character rec...
Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative m...