Semi-supervised classifier design that simultaneously utilizes both labeled and unlabeled samples is a major research issue in machine learning. Existing semisupervised learning methods belong to either generative or discriminative approaches. This paper focuses on probabilistic semi-supervised classifier design and presents a hybrid approach to take advantage of the generative and discriminati...

This paper presents a classifier for text data samples consisting of main text and additional components, such as Web pages and technical papers. We focus on multiclass and single-labeled text classification problems and design the classifier based on a hybrid composed of probabilistic generative and discriminative approaches. Our formulation considers individual component generative models and...

This paper proposes a self-taught classifier of gateways for hybrid SLAM. Gateways are detected and recognized by the self-taught classifier, which is a SVM classifier and self-taught in that its training samples are produced and labeled without user’s intervention. Since the detection of gateways at the topological boundaries of an acquired metric map reduces computational complexity in partit...

Standard signature verification (SV) systems are writer-dependent (WD), where a specific classifier is designed for each individual. It is inconvenient to ask a user to provide enough number of signature samples to design his WD classifier. In practice, very few samples are collected and inaccurate classifiers maybe produced. To overcome this, writer-independent (WI) systems are introduced. A g...

Background: The time and frequency features of motor unit action potentials (MUAPs) extracted from electromyographic (EMG) signal provide discriminative information for diagnosis and treatment of neuromuscular disorders. However, the results of conventional automatic diagnosis methods using MUAP features is not convincing yet.Objective: The main goal in designing a MUAP characterization system ...

Two experiments were conducted in which the effects of different feedback displays on decision criterion learning were examined in a perceptual categorization task with unequal cost-benefits. In Experiment 1, immediate versus delayed feedback was combined factorially with objective versus optimal classifier feedback. Immediate versus delayed feedback had no effect. Performance improved signific...

A new technique presents to improve the performance of dynamic voltage restorer (DVR) for voltage sag mitigation. This control scheme is based on cuckoo search algorithm with tree fuzzy rule based classifier (CSA-TFRC). CSA is used for optimizing the output of TFRC so the classification output of the network is enhanced. While, the combination of cuckoo search algorithm, fuzzy and decision tree...