We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labeled and unlabeled training data. Our approach is based on extending information regularization framework to boosting, bearing loss functions that combine log loss on labeled data with the information-theoretic measures to encode unlabeled data. Even though the information-theoretic regularization terms make the optimization non-convex, we propose simple sequential gradient descent optimization algorithms, and obtain impressively improved results on synthetic, benchmark and real world tasks over supervised boosting algorithms which use the labeled data alone and a state-of-the-art semi-supervised boosting algorithm.

The aim of the book is to present a cognitive descriptive model of design, considering design as the construction of representations, both internal (e.g., mental models) and external (e.g., drawing...

Association rules mining is a popular data mining modeling tool. It discovers interesting associations or correlation relationships among a large set of data items, showing attribute values that occur frequently together in a given dataset. Despite their great potential benefit, current association rules modeling tools are far from optimal. This article studies how visualization techniques can be applied to facilitate the association rules modeling process, particularly what visualization elements should be incorporated and how they can be displayed. Original designs for visualization of rules, integration of data and rule visualizations, and visualization of rule derivation process for supporting interactive visual association rules modeling are proposed in this research. Experimental results indicated that, compared to an automatic association rules modeling process, the proposed interactive visual association rules modeling can significantly improve the effectiveness of modeling, enhance understanding of the applied algorithm, and bring users greater satisfaction with the task. The proposed integration of data and rule visualizations can significantly facilitate understanding rules compared to their nonintegrated counterpart.