In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

Support vector machine (SVM) has been used widely for classification of
electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as …

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine
learning for classification and regression problems. It relies on vectorized input data …

Objective: This paper describes a data-analytic modeling approach for the prediction of
epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity …

Feature selection in classification is an important task in machine learning. Inspired by the
success of Universum support vector machine proposed by Weston et al. on improving the …

The Universum, which is defined as the sample not belonging to either class of the
classification problem of interest, has been proved to be helpful in supervised learning. In …

Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning
system capable of alerting patients prior to seizures to allow the patient to adjust activities or …

Imbalanced problems often occur when the size of majority class is bigger than that of the
minority one. The Least squares support vector machine (LSSVM) is an effective method for …

Facial expressions are one of the most important characteristics of human behaviour. They
are very useful in applications on human computer interaction. To classify facial emotions …

Many machine learning applications involve analysis of high-dimensional data, where the
number of input features is larger than/comparable to the number of data samples. Standard …