“Building” exact confidence nets

Exact Confidence Intervals

Exact Convex Confidence-Weighted Learning

Confidence-weighted (CW) learning [6], an online learning method for linear clas-sifiers, maintains a Gaussian distributions over weight vectors, with a covariancematrix that represents uncertainty about weights and correlations. Confidenceconstraints ensure that a weight vector drawn from the hypothesis distributioncorrectly classifies examples with a specified probability. Wit...

Exact short Poisson confidence intervals

The authors propose a new method for constructing a confidence interval for the expectation θ of a Poisson random variable. The interval they obtain cannot be shortened without the infimum over θ of the coverage probability falling below 1 − α. In addition, the endpoints of the interval are strictly increasing functions of the observed variable. An easy-to-program algorithm is provided for comp...

Exact Soft Confidence-Weighted Learning

In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (ii...

Building Triangulations Using -Nets

This work addresses the problem of approximating a manifold by a simplicial mesh, and the related problem of building triangulations for the purpose of piecewise-linear approximation of functions. It has long been understood that the vertices of such meshes or triangulations should be “well-distributed,” or satisfy certain “sampling conditions.” This work clarifies and extends some algorithms f...