Exploring Sequential Learning Approaches for Optimum-Path Forest
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| Full text | |
| Author(s): |
Castelo-Fernandez, Cesar
;
de Rezende, Pedro J.
;
Falcao, Alexandre X.
;
Papa, Joao Paulo
;
Bloch, I
;
Cesar, RM
Total Authors: 6
|
| Document type: | Journal article |
| Source: | Lecture Notes in Computer Science; v. 6419, p. 3-pg., 2010-01-01. |
| Abstract | |
In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OFF while still gaining in classification time, at the expense of a slight increase in training time. (AU) | |
| FAPESP's process: | 07/52015-0 - Approximation methods for visual computing |
| Grantee: | Jorge Stolfi |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 09/16206-1 - New trends on optimum-path forest-based pattern recognition |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 07/52015-0 - Approximation methods for visual computing |
| Grantee: | Jorge Stolfi |
| Support Opportunities: | Research Projects - Thematic Grants |