Pattern Recognition and Image Analysis
ISSN 1054-6618 (Print)
ISSN 1555-6212 (Online)
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Keywords
Euclidean space
algebraic approach
classification
clustering
computer vision
data mining
deep learning
edge detection
face recognition
feature extraction
image analysis
image processing
image segmentation
machine learning
neural networks
object detection
pattern recognition
recognition
segmentation
support vector machine
wavelet transform
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Keywords
Euclidean space
algebraic approach
classification
clustering
computer vision
data mining
deep learning
edge detection
face recognition
feature extraction
image analysis
image processing
image segmentation
machine learning
neural networks
object detection
pattern recognition
recognition
segmentation
support vector machine
wavelet transform
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Author Details
Author Details
Ryazanov, V. V.
Issue
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Title
File
Vol 26, No 2 (2016)
Mathematical Method in Pattern Recognition
Optimisation of multiclass supervised classification based on using output codes with error-correcting
Vol 26, No 3 (2016)
Methematical Method in Pattern Recognition
Multilevel models for solution of multiclass recognition problems
Vol 26, No 3 (2016)
Applied Problems
Methods for discrete analysis of medical data on the basis of recognition theory and some of their applications
Vol 27, No 3 (2017)
Mathematical Method in Pattern Recognition
Application driven inverse type constraint satisfaction problems
Vol 28, No 2 (2018)
Mathematical Method in Pattern Recognition
On Finding the Maximum Feasible Subsystem of a System of Linear Inequalities
Vol 28, No 4 (2018)
Proceedings of the 6th International Workshop
Shape of Basic Clusters: Using Analogues of Hough Transform in Higher Dimensions
Vol 28, No 4 (2018)
Mathematical Method in Pattern Recognition
On Some Transformations of Features in Machine Learning in Medicine
Vol 29, No 3 (2019)
Mathematical Method in Pattern Recognition
On a Classification Method for a Large Number of Classes
Vol 29, No 4 (2019)
Applied Problems
A Method for Predicting Rare Events by Multidimensional Time Series with the Use of Collective Methods
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