In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Systems (XCS), Artificial Immune Systems (AIS), Neuro Connectionist Q-Learning (NQL) and Learning Cl...

The development and use of content-based retrieval techniques for 3-D models is a relatively new departure in multimedia retrieval. We have extended our existing multimedia museum information system to support content-, metadataand concept-based retrieval of 3D models of museum artifacts and in this paper we describe a “classifier agent” to automatically assign associations between 3-D artifact...

In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Systems (XCS), Artificial Immune Systems (AIS), Neuro Connectionist Q-Learning (NQL) and Learning Cl...

Every person is unique. This uniqueness is not only prevalent in his/her biometric traits, but also in the way he/she interacts with a biometric device. A recent trend in tailoring a biometric system to each user (client) is by normalizing the match score for each claimed identity. This technique is called user(or client-) specific score normalization. This concept can naturally be extended to ...

The application of methods of machine learning is popular in Bioinformatics. Many problems in Bioinformatics can be regarded as pattern recognition problems and well-known methodologies can be used. Until now those methodologies concentrated on the use of a single genome. With the availability of datasets that contain genomes from several closely related species, information that was derived fr...

There are few contributions to robot autonomous navigation applying Learning Classifier Systems (LCS) to date. The primary objective of this work is to analyse the performance of the strength-based LCS and the accuracy-based LCS, named EXtended Learning Classifier System (XCS), when applied to two distinct robotic tasks. The first task is purely reactive, which means that the action to be perfo...

In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, ...

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...

This paper discusses a new computational scheme based on Functional Networks and applies it to the problem of classification and quantification of gas species in a mixture. A Generalized Functional Network as a new classifier is proposed in order to improve the potentialities of the standard Functional Network classifier. Both methodology and learning algorithm are derived. The performance of t...

Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector ...