| Bibtype |
Article |
| Bibkey |
Khot/etal/2015a |
| Author |
Khot, Tushar and Natarajan, Sriraam and Kersting, Kristian and Gutmann, Bernd and Shavlik, Jude |
| Ls8autor |
Kersting, Kristian
|
| Title |
Gradient-based Boosting for Statistical Relational Learning: The Markov Logic Network and Missing Data Cases |
| Journal |
Machine Learning Journal (MLJ) |
| Abstract |
We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online learning algorithm with smooth convex loss functions that competes with a larger class of regression functions. Our main result is an online gradient boosting algorithm that converts a weak online learning algorithm into a strong one where the larger class of functions is the linear span of the base class. We also give a simpler boosting algorithm that converts a weak online learning algorithm into a strong one where the larger class of functions is the convex hull of the base class, and prove its optimality.
|
| Year |
2015 |
