Results 211 to 220 of about 21,099 (238)
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Analysis of euclidean distance and manhattan distance measure in face recognition
Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), 2013The face expression recognition problem is challenging because different individuals display the same expression differently [1].Here PCA algorithm is used for the feature extraction. Distance metric or matching criteria is the main tool for retrieving similar images from large image databases for the above category of search.
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Using the Manhattan distance for computing the multiobjective Markov chains problem
International Journal of Computer Mathematics, 2017ABSTRACTThis paper presents a novel method for computing the multi-objective problem in the case of a metric state space using the Manhattan distance.
Julio B. Clempner, Alexander S. Poznyak
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Properties of scalable distance minimization problems using the Manhattan metric
2015 IEEE Congress on Evolutionary Computation (CEC), 2015In multi-objective optimization, scalable test problems are required to test and compare the search abilities of the algorithms in solving large and small-dimensional problems. In this paper, we analyze a generalized Distance Minimization Problem (DMP) that is scalable in the number of decision variables and objectives and can be used with any distance
Heiner Zille, Sanaz Mostaghim
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Similarity Search by Generating Pivots Based on Manhattan Distance
2014We address a problem of improving the search efficiency of range queries based on Manhattan distance. To this end, we propose a new pivot generation method (the PGM method) formulated as an iterative algorithm, where its convergence is guaranteed within a finite number of iterations.
Eri Kobayashi +3 more
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Size-constrained 2-clustering in the plane with Manhattan distance. [PDF]
, 2014 We present an algorithm for the 2-clustering problem with cluster size constraints in the plane assuming l1-norm, that works in O(n3logn) time and O(n) space. Such a procedure also solves a full version of the problem, computing the optimal solutions for all possible constraints on cluster sizes. The algorithm is based on a separation result concerning
A. Bertoni, M. Goldwurm, J. Lin, L. Pini
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Balance, Manhattan norm and Euclidean distance of industrial policies for the US
Structural Change and Economic Dynamics, 2011Abstract The design of policy controls oriented to stimulate specific industrial activities highlights a set of problems that involve the choice of the macro variables that make up the policy control, the determination of their aggregate amount as well as their sectoral composition and their inner balance.
CIASCHINI, Maurizio +2 more
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Topology preservation in SOFM: An euclidean versus manhattan distance comparison
1999The Self-Organising Feature Map (SOFM) is one of the unsupervised neural models of most widespread use. Several studies have been carried out in order to determine the degree of topology-preservation for this data projection method, and the influence of the distance measure used, usually Euclidean or Manhattan distance.
Nicolás J. Medrano-Marqués +1 more
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