no code implementations • NeurIPS 2011 • Phil Long, Rocco Servedio
We describe a simple algorithm that runs in time poly(n, 1/gamma, 1/eps) and learns an unknown n-dimensional gamma-margin halfspace to accuracy 1-eps in the presence of malicious noise, when the noise rate is allowed to be as high as Theta(eps gamma sqrt(log(1/gamma))).
no code implementations • NeurIPS 2011 • Phil Long, Rocco Servedio
Our main negative result deals with boosting, which is a standard approach to learning large-margin halfspaces.
no code implementations • NeurIPS 2008 • Phil Long, Rocco Servedio
In recent work Long and Servedio LS05short presented a ``martingale boosting'' algorithm that works by constructing a branching program over weak classifiers and has a simple analysis based on elementary properties of random walks.