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arXiv 2019年

作者： Gil-Pons, Reynaldo Ramírez-Cruz, Yunior Trujillo-Rasua, Rolando Yero, Ismael G. Center for Pattern Recognition and Data Mining Patricio Lumumba s/n Santiago de Cuba90500 Cuba Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg 6 av. de la Fonte Esch-sur-AlzetteL-4364 Luxembourg School of Information Technology Deakin University 221 Burwood Hwy BurwoodVIC3125 Australia Departamento de Matemáticas Escuela Politécnica Superior de Algeciras Universidad de Cádiz Av. Ramón Puyol s/n Algeciras11202 Spain

Given a graph G and a subset of vertices S = {w1, . . ., wt} ⊆ V (G), the multiset representation of a vertex u ∈ V (G) with respect to S is the multiset m(u|S) = {|dG(u, w1), . . ., dG(u, wt)}| . A subset of vertices S such that m(u|S) = m(v|S) ⇐⇒ u = v for every u, v ∈ V (G) \ S is said to be a multiset resolving set, and the cardinality of the smallest such set is the outer multiset dimension. We study the general behaviour of the outer multiset dimension, and determine its exact value for several graph families. We also show that computing the outer multiset dimension of arbitrary graphs is NP-hard, and provide methods for efficiently handling particular cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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The analysis for Gait Energy Image based on statistical methods

The analysis for Gait Energy Image based on statistical meth...

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2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016

作者： Rahman, Siti Zaharah Abd Abdullah, Siti Norul Huda Sheikh Nazri, Mohd Zakree Bin Ahmad Pattern Recognition Research Group Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Data Mining and Optimization Group Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9781509028894

Gait features representation is an important step in gait biometric recognition. It represents content of the individual image and the pattern of human walking simultaneously. Discriminative features can provide the most relevant and useful information for individual classification. Many state of the art feature fusions based on average silhouette were used for gait feature representation technique. Those fusion methods are mostly robust in simple case but inadequate for difficult gait representation such as walking with occlusion, walking with different view and walking with briefcase that affect the accuracy of gait performance. For that reason, we are exploring and proposing another potential feature fusion based on statistical method namely edges directed matrix of Gait Energy Image (EDMSGEI). We compare our EDMSGEI method with Gray Level Co-Occurrence Matrix (GLCMGEI) and Histogram of Gaussian (HOGGEI) method for individual recognition with K-Nearest neighbour classifier. The datasets involved are CASIA and UKM-CSM dataset images. Apart from HOGGEI, this study also shows that EDMSGEI gained better gait recognition performance. © 2016 IEEE.

关键词： Statistical methods

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CICE-BCubed: A new evaluation measure for overlapping clustering algorithms

CICE-BCubed: A new evaluation measure for overlapping cluste...

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18th Iberoamerican Congress on pattern recognition, CIARP 2013

作者： Rosales-Méndez, Henry Ramírez-Cruz, Yunior Computer Science Department Universidad de Oriente Santiago de Cuba Cuba Center for Pattern Recognition and Data Mining Universidad de Oriente Santiago de Cuba Cuba

ISBN: (纸本)9783642418211

The evaluation of clustering algorithms is a field of pattern recognition still open to extensive debate. Most quality measures found in the literature have been conceived to evaluate non-overlapping clusterings, even when most real-life problems are better modeled using overlapping clustering algorithms. A number of desirable conditions to be satisfied by quality measures used to evaluate clustering algorithms have been proposed, but measures fulfilling all conditions still fail to adequately handle several phenomena arising in overlapping clustering. In this paper, we focus on a particular case of such desirable conditions, which existing measures that fulfill previously enunciated conditions fail to satisfy. We propose a new evaluation measure that correctly handles the studied phenomenon for the case of overlapping clusterings, while still satisfying the previously existing conditions. © Springer-Verlag 2013.

关键词： Clustering algorithms

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Improvements to Uncalibrated Feature-Based Stereo Matching for Document Images by Using Text-Line Segmentation

Improvements to Uncalibrated Feature-Based Stereo Matching f...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Muhammad Zeshan Afzal Martin Kramer Syed Saqib Bukhari Faisal Shafait Thomas M. Breuel Image Understanding and Pattern Recognition Group Technical University of Kaiserslautern Kaiserslautern Germany Multimedia Analysis and Data Mining Research Group German Research Center for Artificial Intelligence Kaiserslautern Germany

Document images prove to be a difficult case for standard stereo correspondence approaches. One of the major problem is that document images are highly self-similar. Most algorithms try to tackle this problem by incorporating a global optimization scheme, which tends to be computationally expensive. In this paper, we show that incorporation of layout information into the matching paradigm, as a grouping entity for features, leads to better results in terms of robustness, efficiency, and ultimately in a better 3D model of the captured document, that can be used in various document restoration systems. This can be seen as a divide and conquer approach that partitions the search space into portions given by each grouping entity and then solves each of them independently. As a grouping entity text-lines are preferred over individual character blobs because it is easier to establish correspondences. Text-line extraction works reasonably well on stereo image pairs in the presence of perspective distortions. The proposed approach is highly efficient and matches obtained are more reliable. The claims are backed up by showing their practical applicability through experimental evaluations.

关键词： Feature extraction Robustness Three dimensional displays Image segmentation Cameras Solid modeling Stereo vision

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Fast k-NN Classifier for Documents Based on a Graph Structure

Fast k-NN Classifier for Documents Based on a Graph Structur...

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15th Iberoamerican Congress on pattern recognition, CIARP 2010

作者： Artigas-Fuentes, Fernando José Gil-García, Reynaldo Badía-Contelles, José Manuel Pons-Porrata, Aurora Center of Pattern Recognition and Data Mining Universidad de Oriente Santiago de Cuba Cuba Computer Science and Engineering Department Universitat Jaume I Castelló Spain

ISBN: (纸本)9783642166860

In this paper, a fast k nearest neighbors (k-NN) classifier for documents is presented. Documents are usually represented in a high-dimensional feature space, where their terms are treated as features and the weight of each term reflects its importance in the document. There are many approaches to find the vicinity of an object, but their performance drastically decreases as the number of dimensions grows. This problem prevents its application for documents. The proposed method is based on a graph index structure with a fast search algorithm. Its high selectivity permits to obtain a similar classification quality than the exhaustive classifier, with a few number of computed distances. Our experimental results show that our method can be applied to problems of very high dimensionality, such as Text mining. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Nearest neighbor search

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An incremental nested partition method

An incremental nested partition method

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作者： Correa-Morris, Jyrko Ruiz-Shulcloper, José Espinosa-Isidrón, Dustin L. Pons-Porrata, Aurora Advanced Technologies Application Center Cuba Center for Pattern Recognition and Data Mining Universidad de Oriente Cuba

ISBN: (纸本)9781424421756

In this paper, we introduce an incremental nested partition algorithm for finding the inner structuralization of dynamic datasets. Here we use three partition criteria that allow to obtain a hierarchy of clusterings. The algorithm is based on some mathematical properties, which are introduced in the paper. The experimental results over the AFP and TDT2 news collections show the usefulness of our method to reveal different levels of the information hidden in the datasets. © 2008 IEEE.

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Incremental nested partition method

Incremental nested partition method

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International Conference on pattern recognition

作者： Jyrko Correa-Morris Jose Ruiz-Shulcloper Dustin L. Espinosa-Isidron Aurora Pons-Porrata Advanced Technologies Application Center Center for Pattern Recognition and Data Mining Universidad de Oriente

关键词： Clustering algorithms Partitioning algorithms Shape measurement Page description languages pattern recognition data mining Optimization methods Speech recognition Speech analysis data analysis

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A multilingual application for automated essay scoring

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11th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2008

作者： Castro-Castro, Daniel Lannes-Losada, Rocío Maritxalar, Montse Niebla, Ianire Pérez-Marqués, Celia Álamo-Suárez, Nancy C. Pons-Porrata, Aurora Development Center of Applications Technologies and Systems Cuba Department of Languages and Information Systems University of the Basque Country Spain Center for Applied Linguistics Ministerio de Ciencia Tecnología Y Medio Ambiente Cuba Center for Pattern Recognition and Data Mining Universidad de Oriente Cuba

ISBN: (纸本)3540883088

In this paper, we present a text evaluation system for students to improve Basque or Spanish writing skills. The system uses Natural Language Processing techniques to evaluate essays by detecting specific measures. The application uses a client-server architecture and both the interface and the application itself are multilingual. The article also explains how the system can be adapted to evaluate Spanish essays written in Cuban schools. © 2008 Springer-Verlag.

关键词： Students

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TKB-UO: Using sense clustering for WSD 4

TKB-UO: Using sense clustering for WSD

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4th International Workshop on Semantic Evaluations, SemEval 2007

作者： Anaya-Sánchez, Henry Pons-Porrata, Aurora Berlanga-Llavori, Rafael Center of Pattern Recognition and Data Mining Universidad de Oriente Cuba Computer Science Universitat Jaume i Spain

This paper describes the clustering-based approach to Word Sense Disambiguation that is followed by the TKB-UO system at SemEval-2007. The underlying disambiguation method only uses WordNet as external resource, and does not use training data. Results obtained in both Coarse-grained English all-words task (task 7) and English fine-grained all-words subtask (task 17) are presented. © 2007 Association for Computational Linguistics.

关键词： Semantics

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A proposal of a morphological tagger for Spanish based on Cuban corpora

A proposal of a morphological tagger for Spanish based on Cu...

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International Conference Recent Advances in Natural Language Processing, RANLP 2007

作者： García-Moya, Lisette Pons-Porrata, Aurora Ruiz-Miyares, Leonel Center of Pattern Recognition and Data Mining Universidad de Oriente Santiago-de Cuba Cuba Center for Applied Linguistic Santiago-de Cuba Cuba

ISBN: (纸本)9789549174373

In this paper we describe a morphological tagger for Spanish based on Cuban corpora. The tagger combines Hidden Markov Models with some heuristics and dictionaries to provide the appropriate part-of-speech tag for each word in a text document, according to the context in which it appears. Moreover, a morphological analyser that provides all possible morphological interpretations of words is used. It allows us to reduce possible grammatical tags and to obtain not only the appropriate part-of-speech tag, but also its morphological information. The proposed tagger achieves 97.76 % accuracy for a legal corpus.

关键词： Hidden Markov models

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