Yu. I. Zhuravlev

Construction of recognition algorithms correct for a given control sample

Abstract A NUMERICAL scheme is presented for synthesizing a recognition algorithm not making errors on a given control sample, on the assumption that the initial information is represented in a standard form. Algebraic methods are used to construct the algorithm. A subclass of calculation-of-estimates algorithms is used as the initial set.

The use of pattern recognition methods in tasks of biomedical diagnostics and forecasting

Questions of use of pattern recognition methods in medical tasks are discussed. The Recognition program system is presented, which contains a variety of pattern recognition and cluster analysis methods. Some methods based on voting by a system of regularities are discussed in more detail. An example of solved tasks of hysteromyoma relapse forecasting is given.

Measures of the importance of objects in complex systems

Abstract PROBLEMS of the construction of measures of importance for objects forming a system are considered. Some approaches to the introduction of measures of importance are analyzed. A method of calculating measures of importance by means of voting procedures is derived.

On basic problems of image recognition in neurosciences and heuristic methods for their solution

The paper describes the possibilities and main results of mathematical and informational approaches to automating the analysis, recognition, and evaluation of images in brain research. The latter are conducted in such essential sectors of neuroscience as molecular and cellular neuroscience, behavioral neuroscience, systemic neuroscience, developmental neuroscience, cognitive neuroscience, theoretical and computational neuroscience, neurology and psychiatry, neural engineering, neurolinguistics, and neurovisualization. An important direction in simulating diseases, including diseases of the brain and their diagnoses, is the obtaining, storage, processing, and analysis of data extracted from digital images. The theoretical and methodical basis of automating the processing, analysis, and evaluation of experimental data obtained in brain research consists of the mathematical theory of image recognition and mathematical theory of image analysis. The paper presents examples of mathematical and informational approaches to automate the processing, analysis, and evaluation of microimages of neurons for constructing preclinical models of Parkinson’s disease.

Construction of an ensemble of logical correctors on the basis of elementary classifiers

A problem of constructing correct recognition algorithms on the basis of incorrect elementary classifiers is considered. A model of recognition procedures based on the construction of a family of logical correctors is proposed and analyzed. To this end, a genetic approach is applied that allows one, first, to reduce the computational cost and, second, to construct correctors with high recognition ability. This model is tested on real problems.

A model of recognition algorithms with representative samples and systems of supporting sets

Abstract A class of recognition algorithms based on a mixed principle is described: in the construction the basic ideas of both algorithms of “Kora” type and also algorithms of the calculation of estimates are used.

Sixty years of cybernetics

Fundamental aspects of cybernetics, such as goals, problems, methods, tools, brief history, and correlation with other sciences, are considered. Cybernetics in its classical interpretation is the science of information management, communication, and processing. As cybernetics developed, this definition was formalized as the science of methods and processes of information acquisition, storage, processing, analysis, and evaluation, which allows it to apply to decision making in complex control systems. These systems include all engineering, biological, administrative, social, ecological, and economical systems. The main thesis that determined the goals, problems, subject matter, and development of cybernetics as a whole up to the present is the similarity in management and communication processes in machines, living organisms, and both animal and human societies. First of all, these are processes of transfer, storage, and processing of information, i.e., various signals, messages, and data. Any signal and any information may be considered independently from its particular content and destination as a certain choice between two or more values having the known probabilities (selective concept of information). It allows us to treat all processes on the basis of a unified measure and statistical apparatus. The idea of the general theory of control and communication, that is, cybernetics, is based on this hypothesis.

Computer science: Subject, fundamental research problems, methodology, structure, and applied problems

The work is devoted to computer science. The subject, fundamental research problems, methodology, structure, and applied problems are defined and analyzed. The mathematical apparatus of computer science and its main methods—formalization, algorithmization, mathematical modeling, and programming—are considered. A characterization is given to the main fields of computer science pattern recognition, image analysis, artificial intelligence, intelligent data analysis, and information technologies. An in-depth analysis is carried out of the relationship and interaction between computer science and cybernetics. The role and the subject of informatics are discussed.

Analysis of a training sample and classification in one recognition model

A problem of classification by precedents in partial precedence models is considered. An algorithm is presented for searching for maximum logical regularities of a class (LRCs) for consistent training tables. A two-level solution scheme of a problem is proposed for finding an optimal decision rule. First, LRCs are obtained by training data, and a mapping of the initial feature descriptions of objects into a space of points of a discrete unit cube is constructed. The objects of the training sample can be divided by a hyperplane in the latter space. It is suggested that a linear decision rule in the latter space that provides the maximum gap, similar to the support vector method, should be used as the decision rule.

Practical algorithms for algebraic and logical correction in precedent-based recognition problems

Practical precedent-based recognition algorithms relying on logical or algebraic correction of various heuristic recognition algorithms are described. The recognition problem is solved in two stages. First, an arbitrary object is recognized independently by algorithms from a group. Then a final collective solution is produced by a suitable corrector. The general concepts of the algebraic approach are presented, practical algorithms for logical and algebraic correction are described, and results of their comparison are given.