Journal Article10.1103/PHYSREVE.74.026703
Deterministic walks as an algorithm of pattern recognition.
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TL;DR: In this article, a deterministic procedure is proposed to find attractors of mutually close points based on the neighborhood ranking, and a memory parameter is used as a hierarchy parameter in which the clusters are identified.
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Abstract: New tools for automatically finding data clusters that share statistical properties in a heterogeneous data set are imperative in pattern recognition research. Here we introduce a deterministic procedure as a tool for pattern recognition in a hierarchical way. The algorithm finds attractors of mutually close points based on the neighborhood ranking. A memory parameter $\ensuremath{\mu}$ acts as a hierarchy parameter, in which the clusters are identified. The final result of the method is a general tree that represents the nesting structure of the data in an invariant way by scale transformation.
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Citations
Texture analysis using graphs generated by deterministic partially self-avoiding walks
TL;DR: This work presents an approach to generate graphs out of the trajectories produced by the tourist walks, which embody important characteristics related to tourist transitivity in the image.
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High-level pattern-based classification via tourist walks in networks
TL;DR: A hybrid classification technique, which combines the decisions of low- and high-level classifiers, is presented and it is suggested that the proposed technique is able to improve the already optimized performance of traditional classification techniques.
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Lévy-like behaviour in deterministic models of intelligent agents exploring heterogeneous environments
TL;DR: In this article, a deterministic walker, visiting sites randomly distributed on the plane and with varying weight or attractiveness, minimizes a function that depends on the distance to the next unvisited target (cost) and the weight of that target (gain).
An image analysis methodology based on deterministic tourist walks
Monica Guimaraes Campiteli,Alexandre Souto Martinez,Odemir Martinez Bruno +2 more
- 23 Oct 2006
TL;DR: The deterministic walk technique is presented and its results for two experiments using Brodatz images are presented and the method proposed explores the set in all scales and is able to characterize efficiently different texture classes.
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Complex network classification using partially self-avoiding deterministic walks.
TL;DR: A new measurement for complex network classification based on partially self-avoiding walks is presented and it is shown that the proposed measurement improves correct classification of networks compared to the traditional ones.
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