We study the convergence and the rate of convergence of a local manifold learning algorithm: LTSA [13]. The main technical tool is the perturbation analysis on the linear invarian...
— We present an ant model that solves a discrete foraging problem. We describe simulations and provide a complete convergence analysis: we show that the ant population computes t...
The asymptotic behavior of stochastic gradient algorithms is studied. Relying on some results of differential geometry (Lojasiewicz gradient inequality), the almost sure pointconve...
We present a simple ant model that solves a discrete foraging problem. We describe simulations and provide a complete convergence analysis: we show that the ant population compute...
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as...
Ayhan Demiriz, Kristin P. Bennett, John Shawe-Tayl...