In engineering, the word interaction and learning can cover many aspects of processes of intelligent systems. In this paper, we regard the process of the interaction as transition of the status of an interface between them. From this point of view, we give a formal model of interaction and learning based on a learning system called classifier system. Firstly, we show a generall view of interact...

Support vector machines (SVM) have been successfully applied in numerous areas of pattern recognitions, and have demonstrated excellent performance. However, traditional SVM does not make efficient use of both labeled training data and unlabeled testing data. Moreover, one usually encounters high dimensional and nonlinear distributed data in classification problems, especially in financial cred...

Human detection is the first step for a number of applications such as smart video surveillance, driving assistance systems, and intelligent digital content management. It’s a challenging problem due to the variance of illumination, color, scale, pose, and so forth. This chapter reviews various aspects of human detection in static images and focuses on learning-based methods that build classifi...

This paper presents a new architecture of a classifier system for learning in virtual environments. The model will be integrated in our multi-user platform to provide interaction between intelligent agents and user clones. An agent is an autonomous entity equipped with sensors and effectors. Its behavior is guided by rewards coming from the environment that produce rules called classifiers. The...

Microalbuminuria (MA) is an independent predictor of cardiovascular and renal disease, development of overt nephropathy, and cardiovascular mortality in patients with type 2 diabetes. Detecting MA is an important screening tool to identify people with high risk of cardiovascular and kidney disease. The gold standard to detect MA is measuring 24-h urine albumin excretion. A new method for MA dia...

Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behaviour or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with ...

The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural...

(MCS), but this has rarely incorporated any utilisation of weightless neural systems(WNS) as the combiner of an MCS ensemble. This paper explores the application of weightless networks within the multi-classifier environment by introducing an intelligent multi-classifier system using a WNS called the Enhanced Probabilistic Convergent Neural Networks (EPCN). The paper explores the use of EPCN by...

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and dec...