Advanced · Research
Co-training
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Co-training is a semi-supervised learning method using two independent model views to train on large unlabeled datasets.
Technical Definition
Co-training is a semi-supervised learning method using two independent model views to train on large unlabeled datasets.
How it works
Co-training is a semi-supervised learning technique useful when dealing with a high ratio of unlabeled to labeled data. It requires the data to have two distinct, independent, and complementary sets of features. The method trains two classifiers on these different feature sets, using the predictions on unlabeled data from one classifier to augment the training set for the other.
Related Concepts
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Classification — A supervised learning task where the model assigns inputs to discrete categories.
- Semi-supervised Learning — A hybrid approach that uses a small amount of labeled data alongside a large amount of unlabeled data.