Intelligent pattern selection is an active learning strategy where the classifiers select during training the most informative patterns. This paper investigates such a strategy where the informativeness of a pattern is measured as the approximation error produced by the classifier. The algorithm builds the training corpus starting from a small randomly chosen initial dataset and new patterns ar...

Unbalanced, high-dimensional, binary classifications create challenges in a variety of systems and environments, most notably within physical security and computer security domains. The imbalance within these problems consists of a significant majority of the negative (healthy, non-intruding) class and a minority (unhealthy, intruding) positive class. Any system that needs protection from malic...

In this work we present InFeRno, an intelligent web pornography elimination system, classifying web pages based solely on their visual content. The main characteristics of our system include: (i) a powerful vector space with a small but sufficient number of features that manage to improve the discriminative ability of the SVM classifier; (ii) an extra class (bikini) that strengthens the perform...

In this paper we present some aspects of an intelligent interface for a WWWeb legal information retrieval system. Our system is able to keep the context of the user interaction in order to supply suggestions for further refinement of the user queries. The set of documents obtained from the user queries is dynamically organised in clusters labeled with keywords from a juridical thesaurus. Since,...

In semantic-based image classification, learning concepts for adding knowledge to the image descriptions is an issue of special interest. This learning increases the capabilities for more “intelligent” image processing. The classifier learns by generalizing specific facts present in a number of design samples. Due to the fact that the learning and classification processes run over image descrip...

While Intelligent Tutoring Systems (ITSs) are often informed by the data extracted from tutoring corpora, coding schemes can be time consuming to implement. Therefore, an automatic classifier may make for quicker classifications. Dialogue from expert tutoring sessions were analyzed using a topic model to investigate how topics mapped on to pre-existing coding schemes of different granularities....

Traffic control in large cities is a difficult and non-trivial optimization problem. This paper presents a optimized classifier system using fuzzy logic for recognize and traffic signal control in a single intersection. Also this paper presents preliminary work into designing an intelligent local controller primarily for distributed traffic control systems by fuzzy logic. The idea is to use a c...

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper prese...

Millions of figures appear in biomedical articles, and it is important to develop an intelligent figure search engine to return relevant figures based on user entries. In this study we report a figure classifier that automatically classifies biomedical figures into five predefined figure types: Gel-image, Image-of-thing, Graph, Model, and Mix. The classifier explored rich image features and int...

Meta-cognition allows one to monitor and adaptively control cognitive processes. It guides people to select, evaluate, revise, and abandon cognitive tasks, goals, and strategies. Also, meta-cognition can play an important role in human-like software agents. It includes meta-cognitive knowledge, meta cognitive monitoring, and meta cognitive regulation. The main purpose of this research paper is ...