Classification of a Sequence of Objects with the Fuzzy Decoding Method
The problem of recognition of a sequence of objects (e.g., video-based image recognition, phoneme recognition) is explored. The generalization of the fuzzy phonetic decoding method is proposed by assuming the distribution of the classified object to be of exponential type. Its preliminary phase includes association of each model object with the fuzzy set of model classes with grades of membership defined as the confusion probabilities estimated with the Kullback-Leibler divergence between model distributions. At first, each object (e.g., frame) in a classified sequence is put in correspondence with the fuzzy set which grades are defined as the posterior probabilities. Next, this fuzzy set is intersected with the fuzzy set corresponding to the nearest neighbor. Finally, the arithmetic mean of these fuzzy intersections is assigned to the decision for the whole sequence. In this paper we propose not to limit the method's usage with the Kullback-Leibler discrimination and to estimate the grades of membership of models and query objects based on an arbitrary distance with appropriate scale factor. The experimental results in the problem of isolated Russian vowel phonemes and words recognition for state-of-the-art measures of similarity are presented. It is shown that the correct choice of the scale parameter can significantly increase the recognition accuracy.
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
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object involves here the apparatus of Formal Concept Analysis. We explain the principle of the algorithm on a toy example and describe experiments with real-world datasets.
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.
The problem of automatic image recognition based on the minimum information discrimination principle is formulated and solved. Color histograms comparison in the Kullback–Leibler information metric is proposed. It’s combined with method of directed enumeration alternatives as opposed to complete enumeration of competing hypotheses. Results of an experimental study of the Kullback-Leibler discrimination in the problem of face recognition with a large database are presented. It is shown that the proposed algorithm is characterized by increased accuracy and reliability of image recognition.
In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.
The problem of management of the nonlinear object which is exposed to impact of uncontrollable indignations, is considered in a key of differential game. Synthesis of optimum managements is made with application of transformation of the nonlinear equation of initial object in the differential equation with the parameters depending on a condition. The square-law functional of quality allows to formulate synthesis conditions in the form of need of search of solutions of the equation of Rikkati. The solution of the equation of Rikkati with the parameters depending on a condition, is in a symbolical view with application of algebraic methods that allows to generalize a number of earlier published theoretical results, to receive rather constructive decisions in a number of statements of problems of management.
The article is based upon the fact that the growing demand for master data management systems has not yet produced a commonly accepted metodology for their design and development/ The article offers two mathematical models? that allow a master data management systems designer a way to formally describe their system before development and verify the system quality by measurements? unique to master data management systems.