Indexing-Based Pattern Recognition

2011 ◽  
Vol 403-408 ◽  
pp. 5254-5259 ◽  
Author(s):  
Alexei Mikhailov
Keyword(s):  
Pattern Recognition ◽  
Pattern Classification ◽  
Classification Problems ◽  
Visual Patterns

The paper discusses the mathematics of pattern indexing and its applications to recognition of visual patterns and classification of objects that are represented by objects-properties matrices. It is shown that (a) pattern indexes can be represented by collections of inverted patterns, (b) solutions to pattern classification problems can be found as intersections of inverted patterns and, thus, matching of original patterns avoided.

THE GENERALIZATION OF A CONSTRUCTIVE ALGORITHM IN PATTERN CLASSIFICATION PROBLEMS

1992 ◽  
Vol 03 (supp01) ◽  
pp. 65-70 ◽  
Author(s):  
Neil Burgess ◽  
Silvano Di Zenzo ◽  
Paolo Ferragina ◽  
Mario Notturno Granieri
Keyword(s):  
Principal Component Analysis ◽  
Maximum Likelihood ◽  
Pattern Classification ◽  
Principal Component ◽  
Network Size ◽  
Classification Problems ◽  
Constructive Algorithm ◽  
Maximum Likelihood Procedure ◽  
Optimal Network

The use of a constructive algorithm for pattern classification is examined. The algorithm, a ‘Perceptron Cascade’, has been shown to converge to zero errors whilst learning any consistent classification of real-valued pattern vectors (Burgess, 1992). Limiting network size and producing bounded decision regions are noted to be important for the generalization ability of a network. A scheme is suggested by which a result on generalization (Vapnik, 1992) may enable calculation of the optimal network size. A fast algorithm for principal component analysis (Sirat, 1991) is used to construct ‘hyper-boxes’ around each class of patterns to ensure bounded decision regions. Performance is compared with the Gaussian Maximum Likelihood procedure in three artificial problems simulating real pattern classification applications.

scholarly journals NEURAL NETWORKS IN EXPERIMENTAL HIGH-ENERGY PHYSICS

1992 ◽  
Vol 03 (04) ◽  
pp. 733-771 ◽  
Author(s):  
C. BORTOLOTTO ◽  
A. DE ANGELIS ◽  
N. DE GROOT ◽  
J. SEIXAS
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
High Energy Physics ◽  
High Energy ◽  
Classification Problems ◽  
The Status ◽  
Artificial Neural ◽  
Energy Physics

During the last years, the possibility to use Artificial Neural Networks in experimental High Energy Physics has been widely studied. In particular, applications to pattern recognition and pattern classification problems have been investigated. The purpose of this article is to review the status of such investigations and the techniques established.

FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS

2008 ◽  
Vol 22 (08) ◽  
pp. 1461-1486 ◽  
Author(s):  
G. A. PAPAKOSTAS ◽  
Y. S. BOUTALIS ◽  
D. E. KOULOURIOTIS ◽  
B. G. MERTZIOS
Keyword(s):  
Pattern Recognition ◽  
Pattern Classification ◽  
Degrees Of Freedom ◽  
Cognitive Maps ◽  
Classification Problem ◽  
Research Field ◽  
Fuzzy Cognitive Maps ◽  
Classification Problems ◽  
Hybrid Classifier ◽  
Classification Structure

A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

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