We derive an equivalence between AdaBoost and the dual of a convex optimization problem, showing that the only difference between minimizing the exponential loss used by AdaBoost ...
Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is ...
This paper treats a new approach to the problem of periodic signal estimation. The idea is to model the periodic signal as a function of the state of a second-order nonlinear ordi...
In inductive logic programming, subsumption is a widely used coverage test. Unfortunately, testing -subsumption is NP-complete, which represents a crucial efficiency bottleneck fo...
Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stag...
Jonathan Pillow, Liam Paninski, Eero P. Simoncelli