This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
We consider a framework for semi-supervised learning using spectral decomposition-based unsupervised kernel design. We relate this approach to previously proposed semi-supervised l...
This paper addresses the initial shift problem in iterative learning control with system relative degree. The tracking error caused by nonzero initial shift is detected when apply...
This paper presents a task selection model for personalised educational instruction. The proposed model is based on the student expertise level and it takes into account performan...
We present a new unsupervised learning technique for the discovery of temporal clusters in large data sets. Our method performs hierarchical decomposition of the data to find stru...