I was going over the Co-training technique, which is a widely used semi-supervised method, where you look at the data in two views (split it into two uncorrelated feature sets) and then learn the classifiers. To predict a new test data you take into account the decision of both the learned classifiers. This explanation sounds similar to in the way random forest works. Have I interpreted it wrong?
That is right Eric. They adopt the same philosophy. That is, to diversify the classifiers and get more reliable predictions.
However, in co-trainig one classifier learns with the samples detected by the other classifier. But, in the random forest each classifier (tree) learns independently from the other.
Dear Eric,
if you have interest in how this algorithm works, please check this youtube video
https://www.youtube.com/watch?v=loNcrMjYh64
Regards
Whereas "Co-training" is a weak-learner approach, it has an objective function making it more analogous to Boosting.
- Badges
- Science topic
- Similar topics
- Data Mining
- Classification