Co-training by Committee: A New Semi-supervised Learning Framework

By Mohamed Farouk Abdel Hady, Friedhelm Schwenker. 2008

52 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Yuji Roh
5. Steven Euijong Whang
6. Geon Heo
7. Alberto Fernández
8. Enrique Herrera-Viedma

In Computer Vision and Pattern Recognition

1. Salvador Garcia
2. Yuji Roh
3. Francisco Herrera
4. Isaac Triguero
5. Geon Heo
6. Steven Euijong Whang

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “ two models must agree with each other. Compared to Co-training, Tri-training does not require sufficient and redundant views, which makes it more widely applicable. Co-training by Committee (Bagging) [71] is similar to Tritraining, but uses an ensemble of any number of models and other ensemble lear”
• 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 [”