Abstract: The numerical restrictions of Bayesian method of pattern recognition are investigated. The probability maximum of correspondence of local state to one of the basis patterns is defined through the expansion of examined vector over the patterns. The practical example of recognition without errors is presented in the frame of nearest neighbor method for the case, when the probability interpretation of the expansion coefficients is not valid. The spectral portraits of solving matrices are constructed for the literature texts author identification problem.Note: Research direction:Mathematical modelling in actual problems of science and technic
This paper presents an interactive model for structural pattern recognition based on a naïve Bayes c...
This course covers topics related to the recognition of unknown patterns based on distinguishing pro...
Nearest neighbour algorithms are among the most popular methods used in statistical pattern recognit...
The paper presents numerical restrictions of Bayesian method to a pattern recognition. The maximum p...
All Rights Reserved © 1996 Springer Science+Business Media Dordrecht Originally published by Kluwe...
Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an outp...
The book presents approximate inference algorithms that permit fast approximate answers in situation...
Abstract: The results of statistical research of the evaluation function, the vector of pr...
Pattern recognition systems play a role in applications as diverse as speech recognition, optical ch...
In this paper, we review some pattern recognition schemes published in recent years. After giving th...
Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. ...
Summary. Automatic pattern recognition is usually considered as an engineer-ing area which focusses ...
The paper presents a methodology to evaluate and compare different algorithms for general pattern re...
Pattern recognition by automata such as digital and/or analog computers essentially consists in reco...
Statistical pattern recognition relates to the use of statistical techniques for analysing data meas...
This paper presents an interactive model for structural pattern recognition based on a naïve Bayes c...
This course covers topics related to the recognition of unknown patterns based on distinguishing pro...
Nearest neighbour algorithms are among the most popular methods used in statistical pattern recognit...
The paper presents numerical restrictions of Bayesian method to a pattern recognition. The maximum p...
All Rights Reserved © 1996 Springer Science+Business Media Dordrecht Originally published by Kluwe...
Closely related to the concept of Machine Learning, Pattern Recognition is the assignment of an outp...
The book presents approximate inference algorithms that permit fast approximate answers in situation...
Abstract: The results of statistical research of the evaluation function, the vector of pr...
Pattern recognition systems play a role in applications as diverse as speech recognition, optical ch...
In this paper, we review some pattern recognition schemes published in recent years. After giving th...
Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. ...
Summary. Automatic pattern recognition is usually considered as an engineer-ing area which focusses ...
The paper presents a methodology to evaluate and compare different algorithms for general pattern re...
Pattern recognition by automata such as digital and/or analog computers essentially consists in reco...
Statistical pattern recognition relates to the use of statistical techniques for analysing data meas...
This paper presents an interactive model for structural pattern recognition based on a naïve Bayes c...
This course covers topics related to the recognition of unknown patterns based on distinguishing pro...
Nearest neighbour algorithms are among the most popular methods used in statistical pattern recognit...