2026-04-17T20:11:49Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1191212025-12-05T09:55:18Zcom_2072_1057col_2072_478917col_2072_478920

00925njm 22002777a 4500 dc Puertas i Prats, Eloi author Escalera Guerrero, Sergio author Pujol Vila, Oriol author 2018-01-18T13:43:24Z 2018-01-18T13:43:24Z 2015-04-30 2018-01-18T13:43:24Z In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. Algorismes Aprenentatge Algorithms Learning Generalized multi-scale stacked sequential learning for multi-class classification