Graph Construction for Semi-Supervised Learning
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4343-4343.
Abstract
Semi-Supervised Learning (SSL) techniques have become very relevant since they require a small set of labeled data. In this scenario, graph-based SSL algorithms provide a powerful framework for modeling manifold structures in high-dimensional spaces and are effective for the propagation of the few initial labels present in training data through the graph. An important step in graph-based SSL methods is the conversion of tabular data into a weighted graph. The graph construction has a key role in the quality of the classification in graph-based methods. Nevertheless, most of the SSL literature focuses on developing label inference algorithms without studying graph construction methods and its effect on the base algorithm performance. This PhD project aims to study this issue and proposes new methods for graph construction from ï¬≠at data and improves the performance of the graph-based algorithms.
@inproceedings{ijcai2015p619,
title = {Graph Construction for Semi-Supervised Learning},
author = {Berton, Lilian and Lopes, Alneu de Andrade},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4343--4343},
year = {2015},
month = {7},
note = {Doctoral Consortium},
url = {https://ijcai.org/proceedings/2015/619},
}
TY - CPAPER TI - Graph Construction for Semi-Supervised Learning AU - Berton, Lilian AU - Lopes, Alneu de Andrade T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 4343 EP - 4343 UR - https://ijcai.org/proceedings/2015/619 ER -Copied
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