Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Linear Programming Boosting via Column Generation
Machine Learning
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
An introduction to boosting and leveraging
Advanced lectures on machine learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Totally corrective boosting algorithms that maximize the margin
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Linear optimization problems (LPs) with a very large or even infinite number of constraints frequently appear in many forms in machine learning. A linear program with m constraints can be written as where I assume for simplicity that the domain of x is the n dimensional probability simplex . Optimization problems with an infinite number of constraints of the form , for all j∈J, are called semi-infinite, when the index set J has infinitely many elements, e.g. J=ℝ. In the finite case the constraints can be described by a matrix with m rows and n columns that can be used to directly solve the LP. In semi-infinite linear programs (SILPs) the constraints are often given in a functional form depending on j or implicitly defined, for instance by the outcome of another algorithm.