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A multiple feature vector framework for forest species recognition., , , and . SAC, page 16-20. ACM, (2013)Multi-scale Forest Species Recognition Systems for Reduced Cost., , and . CoRR, (2017)Handwritten Brazilian month recognition: an analysis of two NN architectures and a rejection mechanism., , and . IWFHR, page 209-214. IEEE Computer Society, (2004)Handwritten Recognition with Multiple Classifiers for Restricted Lexicon., , , , and . SIBGRAPI, page 82-89. IEEE Computer Society, (2004)A PSO-based framework for dynamic SVM model selection., , and . GECCO, page 1227-1234. ACM, (2009)Adaptive Incremental Learning with an Ensemble of Support Vector Machines., , and . ICPR, page 4048-4051. IEEE Computer Society, (2010)Multi-scale texture recognition systems with reduced cost: A case study on forest species., , and . SMC, page 1316-1321. IEEE, (2017)Automatic forest species recognition based on multiple feature sets., , , and . IJCNN, page 1296-1303. IEEE, (2014)An empirical study on diversity measures and margin theory for ensembles of classifiers., , and . FUSION, page 1-8. IEEE, (2007)Evaluating the Conventional and Class-Modular Architectures Feedforward Neural Network for Handwritten Word Recognition., , , and . SIBGRAPI, page 315-322. IEEE Computer Society, (2003)