| Texto completo | |
| Autor(es): |
Valem, Lucas Pascotti
;
Pedronette, Daniel C. G.
;
Breve, Fabricio
;
Guilherme, Ivan Rizzo
;
IEEE
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2018-01-01. |
| Resumo | |
Due to the growing availability of unlabeled data and the difficulties in obtaining labeled data, the use of semi-supervised learning approaches becomes even more promising. The capacity of taking into account the dataset structure is of crucial relevance for effectively considering the unlabeled data. In this paper, a novel classifier is proposed through a manifold learning approach. The graph is constructed based on a new hybrid similarity measure which encodes both supervised and unsupervised information. Next, strongly connected components are computed and used to analyze the dataset manifold. The classification is performed through a voting scheme based on primary (labeled) and secondary (unlabeled) voters. An experimental evaluation is conducted, considering various datasets, diverse situations of training/test dataset sizes and comparison with baselines. The proposed method achieved positive results in most of situations. (AU) | |
| Processo FAPESP: | 16/05669-4 - Segmentação interativa de imagens utilizando competição e cooperação entre partículas |
| Beneficiário: | Fabricio Aparecido Breve |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/08645-0 - Reclassificação e agregação de listas para tarefas de recuperação de imagens |
| Beneficiário: | Daniel Carlos Guimarães Pedronette |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores |
| Processo FAPESP: | 17/02091-4 - Seleção e combinação de métodos de aprendizado não supervisionado para recuperação de imagens por conteúdo |
| Beneficiário: | Lucas Pascotti Valem |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |