Combining Committee-Based Semi-Supervised Learning and Active Learning

By Mohamed Farouk Abdel Hady, Friedhelm Schwenker. 2010

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Francisco Herrera
2. Salvador Garcia
3. Isaac Triguero
4. Xin He
5. Kaiyong Zhao
6. Xiaowen Chu
7. Alberto Fernández
8. Enrique Herrera-Viedma

In Statistical Learning Theory

1. Xin He
2. Francisco Herrera
3. Isaac Triguero
4. Kaiyong Zhao
5. Salvador Garcia
6. Xiaowen Chu

Citations in the corpus

• AutoML: A Survey of the State-of-the-Art (2019, methodology): “Roh et al. [61] have summarized the self-labeling methods into the following categories: self-training [62], [63], co-training [64], [65], and co-learning [66].”
• Neural Network Ensembles in Reinforcement Learning (2015, unknown): “Whereas ensemble or committee-based models have been successfully applied to supervised [6], semisupervised and active learning [8,18,19], in RL not much research has been done to combine agents in a committee.”
• Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, methodology): “Standard self-training Self-Training [13] Standard co-training Co-Training [15] Statistical co-learning Statistical-Co [34] ASSEMBLE ASSEMBLE [36] Democratic co-learning Democratic-Co [37] Self-training with editing SETRED [14] Tri-training TriTraining [20] Tri-training with editing DE-TriTraining [”