Random classification noise defeats all convex potential boosters

Wrapping Boosters against Noise

Wrappers have recently been used to obtain parameter optimizations for learning algorithms. In this paper we investigate the use of a wrapper for estimating the correct number of boosting ensembles in the presence of class noise. Contrary to the naive approach that would be quadratic in the number of boosting iterations, the incremental algorithm described is linear. Additionally, directly usin...

Potential Boosters?

Non-Convex Boosting Overcomes Random Label Noise

The sensitivity of Adaboost to random label noise is a well-studied problem. LogitBoost, BrownBoost and RobustBoost are boosting algorithms claimed to be less sensitive to noise than AdaBoost. We present the results of experiments evaluating these algorithms on both synthetic and real datasets. We compare the performance on each of datasets when the labels are corrupted by different levels of i...

Learning Logic Programs with Random Classification Noise

We consider the learnability of classes of logic programs in the presence of noise, assuming that the label of each example is reversed with a xed probability. We review the polynomial PAC learnability of nonrecursive, determinate, constant-depth Horn clauses in the presence of such noise. This result is extended to an analogous class of recursive logic programs that consist of a recursive clau...

Protocol boosters

This paper describes a new methodology for protocol design, using incremental construction of the protocol from elements called “protocol boosters” on an as-needed basis. Protocol boosters allow: (1) dynamic protocol customization to heterogeneous environments, and (2) rapid protocol evolution. Unlike alternative adaptation approaches, such as link layer, conversion, and termination protocols, ...