| Autor(es): |
Faleiros, Thiago de Paulo
;
Lopes, Alneu de Andrade
;
Yang, Q
;
Wooldridge, M
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI); v. N/A, p. 2-pg., 2015-01-01. |
| Resumo | |
This article presents a bipartite graph propagation method to be applied to different tasks in the machine learning unsupervised domain, such as topic extraction and clustering. We introduce the objectives and hypothesis that motivate the use of graph based method, and we give the intuition of the proposed Bipartite Graph Propagation Algorithm. The contribution of this study is the development of new method that allows the use of heuristic knowledge to discover topics in textual data easier than it is possible in the traditional mathematical formalism based on Latent Dirichlet Allocation (LDA). Initial experiments demonstrate that our Bipartite Graph Propagation algorithm return good results in a static context (offline algorithm). Now, our research is focusing on big amount of data and dynamic context (online algorithm). (AU) | |
| Processo FAPESP: | 11/23689-9 - Propagação em Grafos Bipartidos para Extração de Tópicos em Fluxo de Dados |
| Beneficiário: | Thiago de Paulo Faleiros |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |