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Your search keyword '"Shimomura, Larissa Capobianco"' showing total 4 results
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4 results on '"Shimomura, Larissa Capobianco"'

1. HGraph: A Connected-Partition Approach to Proximity Graphs for Similarity Search

Author

Shimomura, Larissa Capobianco, Kaster, Daniel S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hartmann, Sven, editor, Küng, Josef, editor, Chakravarthy, Sharma, editor, Anderst-Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2019
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2. Performance Analysis of Graph-Based Methods for Exact and Approximate Similarity Search in Metric Spaces

Author

Shimomura, Larissa Capobianco, Vieira, Marcos R., Kaster, Daniel S., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Marchand-Maillet, Stéphane, editor, Silva, Yasin N., editor, and Chávez, Edgar, editor

Published
2018
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4. Towards Proximity Graph Auto-configuration - An Approach Based on Meta-learning

Author

Oyamada, Rafael Seidi, Shimomura, Larissa Capobianco, Junior, Sylvio Barbon, Kaster, Daniel S., Darmont, Jérôme, Novikov, Boris, Wrembel, Robert, Seidi Oyamada, R, Shimomura, R. C., Barbon Junior, S., Kaster, D. S., and Database Group

Subjects
Complex data type, 050101 languages & linguistics, Auto configuration, Computer science, Nearest neighbor search, 05 social sciences, Meta-learning, Proximity graphs, 02 engineering and technology, computer.software_genre, Random forest, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Auto-configuration, Data mining, computer
Abstract

Due to the high production of complex data, the last decades have provided a huge advance in the development of similarity search methods. Recently graph-based methods have outperformed other ones in the literature of approximate similarity search. However, a graph employed on a dataset may present different behaviors depending on its parameters. Therefore, finding a suitable graph configuration is a time-consuming task, due to the necessity to build a structure for each parameterization. Our main contribution is to save time avoiding this exhaustive process. We propose in this work an intelligent approach based on meta-learning techniques to recommend a suitable graph along with its set of parameters for a given dataset. We also present and evaluate generic and tuned instantiations of the approach using Random Forests as the meta-model. The experiments reveal that our approach is able to perform high quality recommendations based on the user preferences.

Published
2020

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