Name
Abstract | ||
|---|---|---|
We present a new class of density estimation models, Structural Maxent models, with feature functions selected from a union of possibly very complex sub-families and yet benefiting from strong learning guarantees. The design of our models is based on a new principle supported by uniform convergence bounds and taking into consideration the complexity of the different sub-families composing the full set of features. We prove new data-dependent learning bounds for our models, expressed in terms of the Rademacher complexities of these sub-families. We also prove a duality theorem, which we use to derive our Structural Maxent algorithm. We give a full description of our algorithm, including the details of its derivation, and report the results of several experiments demonstrating that its performance improves on that of existing L1-norm regularized Maxent algorithms. We further similarly define conditional Structural Maxent models for multi-class classification problems. These are conditional probability models also making use of a union of possibly complex feature subfamilies. We prove a duality theorem for these models as well, which reveals their connection with existing binary and multi-class deep boosting algorithms. |
Year | Venue | Field |
|---|---|---|
2015 | International Conference on Machine Learning | Density estimation,Conditional probability,Duality (mathematics),Computer science,Uniform convergence,Boosting (machine learning),Artificial intelligence,Generalized iterative scaling,Machine learning,Binary number |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Corinna Cortes | 1 | 6574 | 1120.50 |
| Vitaly Kuznetsov | 2 | 68 | 9.33 |
| Mehryar Mohri | 3 | 4502 | 448.21 |
| Umar Syed | 4 | 259 | 18.34 |