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ORCIDLeonardo R. Emmendorfer
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2020 – today
- 2024
[c24]Luís Alvaro de Lima Silva
, Lori R. F. Machado, Leonardo Emmendorfer:
A Case and Cluster-Based Framework for Reuse and Prioritization in Software Testing. SBSI 2024: 41:1-41:10- 2023
- [c23]Eduardo Silveira, Joaquim Assunção, Leonardo Emmendorfer:
Quantile Symbolic Aggregate approXimation: A guaranteed equiprobable SAX. SBBD 2023: 396-401 - [c22]Kesse Jonatas de Jesus, Mateus Osvaldo Klan Pereira, Leonardo Ramos Emmendorfer, Daniel Fernando Tello Gamarra:
A Comparison of Visual SLAM Algorithms ORB-SLAM3 and DynaSLAM on KITTI and TUM Monocular Datasets. SIBGRAPI 2023: 109-114 - [e1]Leonardo Ramos Emmendorfer, Luiz Marcos Garcia Gonçalves:
36th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2003, Rio Grande, RS, Brazil, November 6-9, 2023. IEEE 2023, ISBN 979-8-3503-3873-7 [contents] - 2022
- [j6]Jonata C. Wieczynski, Javier Fumanal-Idocin, Giancarlo Lucca, Eduardo Nunes Borges, Tiago da Cruz Asmus, Leonardo Ramos Emmendorfer, Humberto Bustince, Graçaliz Pereira Dimuro:
d-XC Integrals: On the Generalization of the Expanded Form of the Choquet Integral by Restricted Dissimilarity Functions and Their Applications. IEEE Trans. Fuzzy Syst. 30(12): 5376-5389 (2022) - [c21]Jonata C. Wieczynski, Giancarlo Lucca, Eduardo N. Borges, Leonardo Emmendorfer, Mikel Ferrero-Jaurrieta, Graçaliz Pereira Dimuro, Humberto Bustince:
Applying d-XChoquet integrals in classification problems. FUZZ-IEEE 2022: 1-7 - [c20]Tatiana Taís Schein, Leonardo R. Emmendorfer, Fabiano NobreMendes, Bárbara D. A. Rodriguez, Luís Pedro Melo de Almeida, Vinícius Menezes de Oliveira:
On the Evaluation of Two Methods Applied to the Morphometry of Linear Dunes. ICIAP (3) 2022: 473-479 - 2021
- [j5]Leonardo Ramos Emmendorfer, Anne Magály de Paula Canuto:
A generalized average linkage criterion for Hierarchical Agglomerative Clustering. Appl. Soft Comput. 100: 106990 (2021) - [j4]Leonardo Ramos Emmendorfer, Graçaliz Pereira Dimuro:
A point interpolation algorithm resulting from weighted linear regression. J. Comput. Sci. 50: 101304 (2021) - [c19]Giancarlo Lucca, Eduardo N. Borges, Rafael A. Berri, Leonardo Emmendorfer, Graçaliz Pereira Dimuro, Tiago da Cruz Asmus:
On the Generalizations of the Choquet Integral for Application in FRBCs. BRACIS (1) 2021: 498-513 - [c18]Lucas T. Bonifácio, Giancarlo Lucca, Graçaliz Pereira Dimuro, Eduardo N. Borges, Leonardo R. Emmendorfer, Stefan Cruz Weigert:
Explainable Classification Methods for Fish Species Detection Using Hydroacoustic Data. FUZZ-IEEE 2021: 1-6 - [c17]Thales Vaz Maciel, Leonardo Ramos Emmendorfer:
Cognitive Consistency Models Applied to Data Clustering. ICAISC (2) 2021: 183-191 - [c16]Leonardo Ramos Emmendorfer, Isadora Bicho Emmendorfer, Luís Pedro Melo de Almeida, Deivid Cristian Leal Alves, Jorge Arigony-Neto:
A Self-interpolation Method for Digital Terrain Model Generation. ICCSA (1) 2021: 352-363 - 2020
- [j3]Lutiele M. Godois, Diana Francisca Adamatti, Leonardo R. Emmendorfer:
A multi-agent-based algorithm for data clustering. Prog. Artif. Intell. 9(4): 305-313 (2020) - [c15]Leonardo Ramos Emmendorfer:
On Improving the Efficiency of Majorization-Minorization for the Inference of Rank Aggregation Models. BRACIS 2020: 151-165 - [c14]Leonardo Ramos Emmendorfer:
An Empirical Evaluation of Global Fitness Surrogate Models in Evolutionary Computation. ICAISC (1) 2020: 387-396 - [c13]Leonardo Ramos Emmendorfer, Graçaliz Pereira Dimuro:
A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression. ICCS (2) 2020: 576-589 - [c12]Leonardo Ramos Emmendorfer:
An Empirical Evaluation of Sharpening Filters Applied to Magnified Images. IWSSIP 2020: 187-192 - [c11]Leonardo R. Emmendorfer:
A Conservative Sharpening Filter for Interpolated Images. SIBGRAPI 2020: 93-100
2010 – 2019
- 2019
- [c10]Leonardo Emmendorfer:
An Empirical Evaluation of Two Novel Linkage Criteria for Hierarchical Agglomerative Clustering. BRACIS 2019: 622-626 - 2018
- [j2]Marcus Guimaraes, Leonardo Emmendorfer, Diana Francisca Adamatti:
Persuasive agent based simulation for evaluation of the dynamic threshold line and trigger classification from the Fogg Behavior Model. Simul. Model. Pract. Theory 83: 18-35 (2018) - 2017
- [c9]Caroline Tomasini, Eduardo Nunes Borges, Karina S. Machado, Leonardo R. Emmendorfer:
A Study on the Relationship between Internal and External Validity Indices Applied to Partitioning and Density-based Clustering Algorithms. ICEIS (1) 2017: 89-98 - 2016
- [c8]Caroline Tomasini, Leonardo R. Emmendorfer, Eduardo Nunes Borges, Karina S. Machado:
A methodology for selecting the most suitable cluster validation internal indices. SAC 2016: 901-903 - 2014
- [c7]Silvia S. C. Botelho, Nelson Duarte Filho, Danúbia Bueno Espíndola, Marcos Amaral, Leonardo R. Emmendorfer, Rafael Penna, Enzo Morosini Frazzon, Carlos Eduardo Pereira, Renato Ventura Henriques:
Including operator's skill and environment conditions in IMS. INDIN 2014: 295-300 - 2013
- [c6]Thyago Salvá, Leonardo R. Emmendorfer, Adriano Velasque Werhli:
Inference of Genetic Regulatory Networks Using an Estimation of Distribution Algorithm. BSB 2013: 148-159 - 2010
- [c5]Leonardo R. Emmendorfer:
On the Design of Populational Clustering. IJCCI (ICEC) 2010: 289-292
2000 – 2009
- 2009
- [j1]Leonardo R. Emmendorfer, Aurora T. R. Pozo:
Effective Linkage Learning Using Low-Order Statistics and Clustering. IEEE Trans. Evol. Comput. 13(6): 1233-1246 (2009) - 2008
- [c4]Rafael Caiuta, Aurora T. R. Pozo, Leonardo R. Emmendorfer, Silvia Regina Vergilio:
Selecting software reliability models with a neural network meta classifier. IJCNN 2008: 3747-3754 - [p1]Leonardo R. Emmendorfer, Aurora T. R. Pozo:
A Clustering-Based Approach for Linkage Learning Applied to Multimodal Optimization. Linkage in Evolutionary Computation 2008: 225-248 - 2007
- [c3]Leonardo R. Emmendorfer, Aurora Trinidad Ramirez Pozo:
An empirical evaluation of linkage learning strategies for multimodal optimization. IEEE Congress on Evolutionary Computation 2007: 326-333 - [c2]Leonardo R. Emmendorfer, Aurora Trinidad Ramirez Pozo:
An Incremental Approach for Niching and Building Block Detection via Clustering. ISDA 2007: 303-308 - [i1]Leonardo R. Emmendorfer, Aurora T. R. Pozo:
Effective linkage learning using low-order statistics and clustering. CoRR abs/0710.2782 (2007) - 2005
- [c1]Leonardo R. Emmendorfer, Rodrigo Traleski, Aurora Trinidad Ramirez Pozo:
Using Prior Knowledge to Improve the Performance of an Estimation of Distribution Algorithm Applied to Feature Selection. HIS 2005: 393-398
Coauthor Index
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