A low variance error boosting algorithm

A more robust boosting algorithm

We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm.

Quantification of Variance Error

This paper accurately quantifies the way in which noise induced estimation errors are dependent on model structure, underlying system frequency response, measurement noise and input excitation. This exposes several new principles. In particular, it is shown here that when employing Output–Error model structures in a prediction-error framework, then the ensuing estimate variability in the freque...

Diversity-Based Boosting Algorithm

Boosting is a well known and efficient technique for constructing a classifier ensemble. An ensemble is built incrementally by altering the distribution of training data set and forcing learners to focus on misclassification errors. In this paper, an improvement to Boosting algorithm called DivBoosting algorithm is proposed and studied. Experiments on several data sets are conducted on both Boo...

A Maximum Variance Cluster Algorithm

A Maximum Variance Cluster Algorithm

We present a partitional cluster algorithm that minimizes the sum-of-squared-error criterion while imposing a hard constraint on the cluster variance. Conceptually, hypothesized clusters act in parallel and cooperate with their neighboring clusters in order to minimize the criterion and to satisfy the variance constraint. In order to enable the demarcation of the cluster neighborhood without cr...