Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster
The paper defines an annotated suffix tree (AST) - a data structure used to calculate and store the frequencies of all the fragments of the given string or a collection of strings. The AST is associated with a string to text scoring, which takes all fuzzy matches into account. We show how the AST and the AST scoring can be used for Natural Language Processing tasks. Copyright © by the paper's authors. Copying only for private and academic purposes.
Статья рассматривает теоретические предпосылки построения оптимальной иерархической структуры системы мониторинга критически важных параметров продовольственной безопасности России на основе применения теории нечетких множеств.
The definition of a phoneme as a fuzzy set of minimal speech units from the model database is proposed. On the basis of this definition and the Kullback-Leibler minimum information discrimination principle the novel phoneme recognition algorithm has been developed as an enhancement of the phonetic decoding method. The experimental results in the problems of isolated vowels recognition and word recognition in Russian are presented. It is shown that the proposed method is characterized by the increase of recognition accuracy and reliability in comparison with the phonetic decoding method
An experimental approach was created for the comparative investigation of the cognitive abilities of the glaucous-winged gull (Larus glaucescens) in their natural habitat. The territoriality of gulls during the breeding period and the fact that the gulls inhabiting the territory of the Komandorsky Reserve are practically not in fear of humans allowed us to work with individually recognized birds directly at their nest sites inside the colony. The possibility of using this approach to investigate their cognitive abilities was demonstrated on 24 gulls, in particular, to investigate their abilities for relative size generalization. The first experiment illustrated that the gulls are able to learn to discriminate two pairs of stimuli according to the feature: 'larger' or 'smaller'. They were then given a test to transfer the discriminative rule in which novel combinations of the same stimuli were used. The gulls successfully coped with only a few of these tests. In the next experiment the birds were taught to discriminate four pairs of similar stimuli. The majority of the birds coped with the tests to transfer the discriminative rule both to the novel combinations of familiar stimuli, and also to the novel stimuli of the familiar category (items of different colour and shape). However, none of the birds transferred the discriminative rule to stimuli of a novel category (sets differing by number of components). Thus, in their ability to generalize at a preconceptual level gulls are more comparable with pigeons, whereas large-brained birds (crows and parrots), are capable of concept formation.
Soft Computing (SC) is a consortium of fuzzy logic (FL), neurocomputing (NC), evolutionary computing (EC), probabilistic computing (PC), chaotic computing (CC) and parts of machine learning theory (ML). SC is the foundation for computational intelligence and is leading to the development of numerous hybrid intelligent information, control and decision-making systems. The methodology of computing with words (CW) is an important event in the evolution of cognitive science, natural language processing, artificial intelligence, and different existing scientific theories. This is because CW can enrich the existing scientific theories and the above-mentioned science fields giving them the capability of using natural languages to operate on perception-based information, not only measurement-based information. Indeed in many real-world problems in natural sciences as well as in industrial engineering, economics, and business, often there is a need to deal with both perception and measurement based information. In the case of perception based information, the available information is not precise enough to justify the use of numbers. Such information is usually described in natural languages rather than in strict (idealized) mathematical expressions. So a strong need has appeared for a new approach, theory and technology for the development of knowledge representation, computing, and reasoning tools that allow creation of systems with high MIQ. The sessions of the ICSCCW-2011 will focus on the development and application of Soft Computing technology and computing with words paradigm in system analysis, decision and control.
This volume contains papers presented at the 13th International Conference on Rough Sets, Fuzzy Sets and Granular Computing (RSFDGrC) held during June 25–27, 2011, at the National Research University Higher School of Economics (NRU HSE) in Moscow, Russia. RSFDGrC is a series of scientific events spanning the last 15 years. It investigates the meeting points among the four major disciplines outlined in its title, with respect to both foundations and applications. In 2011, RSFDGrC was co-organized with the 4th International Conference on Pattern Recognition and Machine Intelligence (PReMI), providing a great opportunity for multi-faceted interaction between scientists and practitioners. There were 83 paper submissions from over 20 countries. Each submission was reviewed by at least three Chairs or PC members.We accepted 34 regular papers (41%). In order to stimulate the exchange of research ideas, we also accepted 15 short papers. All 49 papers are distributed among 10 thematic sections of this volume. The conference program featured five invited talks given by Jiawei Han, Vladik Kreinovich, Guoyin Wang, Radim Belohlavek, and C.A. Murthy, as well as two tutorials given by Marcin Szczuka and Richard Jensen. Their corresponding papers and abstracts are gathered in the first two sections of this volume.