Search

Your search keyword '"Oliveira, Luiz"' showing total 152 results
Start Over Author "Oliveira, Luiz" Publisher ieee
152 results on '"Oliveira, Luiz"'

17. Instance Selection for Geometric Semantic Genetic Programming

Author

Luis Fernando Miranda, B. S. Martins Joao Francisco, V. B. Oliveira Luiz Otavio, and Gisele L. Pappa

Subjects
Theoretical computer science, Computer science, Dimensionality reduction, Closeness, Genetic programming, 0102 computer and information sciences, 02 engineering and technology, Semantics, 01 natural sciences, Weighting, Set (abstract data type), 010201 computation theory & mathematics, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Instance selection, Symbolic regression, Curse of dimensionality
Abstract

Geometric Semantic Genetic Programming (GSGP) is a method that exploits the geometric properties describing the spatial relationship between possible solutions to a problem in an n-dimensional semantic space. In symbolic regression problems, n is equal to the number of training instances. Although very effective, the GSGP semantic space can become excessively big in most real applications, where the value of n is high, having a negative impact on the effectiveness of the GSGP search process. This paper tackles this problem by reducing the dimensionality of GSGP semantic space in symbolic regression problems using instance selection methods. Our approach relies on weighting functions—to estimate the relative importance of each instance based on its position with respect to its nearest neighbours—and on dimensionality reduction techniques—to improve the notion of closeness between instances, generating datasets with simplified input spaces. Experiments were performed on a set of 15 datasets and our experimental analysis shows that using instance selection by instance weighting and dimensionality reduction does improve the effectiveness of the search with almost no impact on root mean square error results.

Published
2020

27. Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation.

Author

Maruyama, Teruo M., Oliveira, Luiz S., Britto, Alceu S., and Sabourin, Robert

Abstract

Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

46. Meta-Learning for Fast Classifier Adaptation to New Users of Signature Verification Systems.

Author

Hafemann, Luiz G., Sabourin, Robert, and Oliveira, Luiz S.

Abstract

Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifiers have access to a few genuine signatures for training, during generalization they also need to discriminate forgeries. This is particularly challenging for skilled forgeries, where a forger practices imitating the user’s signature, and often is able to create forgeries visually close to the original signatures. Most work in the literature address this issue by training for a surrogate objective: discriminating genuine signatures of a user and random forgeries (signatures from other users). In this work, we propose a solution for this problem based on meta-learning, where there are two levels of learning: a task-level (where a task is to learn a classifier for a given user) and a meta-level (learning across tasks). In particular, the meta-learner guides the adaptation (learning) of a classifier for each user, which is a lightweight operation that only requires genuine signatures. The meta-learning procedure learns what is common for the classification across different users. In a scenario where skilled forgeries from a subset of users are available, the meta-learner can guide classifiers to be discriminative of skilled forgeries even if the classifiers themselves do not use skilled forgeries for learning. Experiments conducted on the GPDS-960 dataset show improved performance compared to Writer-Independent systems, and achieve results comparable to state-of-the-art Writer-Dependent systems in the regime of few samples per user (5 reference signatures). [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

47. Study of the Effectiveness of Magnetic Shielding for Compact Power Transformers Used on Mobile Applications.

Author

Salvador, Kaue, Harmel, Diogo, Oliveira, Luiz, Cabral, Sergio, and Almaguer, Hugo

Abstract

This paper presents the performance analysis of a new type of leakage flux collector, designed to act as magnetic shielding in compact power transformers for mobile applications. The analysis was made through 3-D finite-element computer program for modeling and simulating the operation of a 33.3 MVA, 69.3/13.8 kV three-phase compact transformer with and without adoption of the proposed leakage flux collector. The results show that due to its shape and its position in the core transformer this type of leakage flux collector efficiently mitigates the undesirable stray losses and hot spots in some key structural parts of the compact transformers, such as tank and beams. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

50. A Brazilian Speech Database

Author

Paulino, Marco A.D., primary, Costa, Yandre M.G., additional, Britto, Alceu S., additional, Svaigen, Alisson R., additional, Aylon, Linnyer B.R., additional, and Oliveira, Luiz E.S., additional

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results