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Found 3 papers, 0 papers with code
Algorithms and hardness results for parallel large margin learning
Our main negative result deals with boosting, which is a standard approach to learning large-margin halfspaces.
Learning large-margin halfspaces with more malicious noise
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))).
Adaptive Martingale Boosting
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.
