Classifying handwriting into a writer demographic category, e.g., gender, age, or handedness of the writer, is useful for more detailed analysis such as writer verification and identification. This paper describes classification into binary demographic categories using document macro features and several different classification methods: a single feed-forward neural network classifier and combi...

Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefree data while some of them are better than others on noisy data. But in reality, ensemble methods that can consistently gain good performance in situations with or without noise are more desirable. In this paper, we pr...

Learning a function of many arguments is viewed from the perspective of high– dimensional numerical quadrature. It is shown that many of the popular ensemble learning procedures can be cast in this framework. In particular randomized methods, including bagging and random forests, are seen to correspond to random Monte Carlo integration methods each based on particular importance sampling strate...

Methods, such as holdout, random subsampling, k-fold cross-validation, and bootstrap, for making error estimation are discussed. Also considered are general techniques, such as bagging and boosting, for increasing model accuracy. Directory • Table of

Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers u...