Pattern recognition problems span a broad range of applications, where each application has its own tolerance on classification error. The varying levels of risk associated with ma...
We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribut...
Michael Muhlbaier, Apostolos Topalis, Robi Polikar
Confidence-Weighted linear classifiers (CW) and its successors were shown to perform well on binary and multiclass NLP problems. In this paper we extend the CW approach for sequen...
Active learning is a machine learning approach to achieving high-accuracy with a small amount of labels by letting the learning algorithm choose instances to be labeled. Most of p...
— We are interested in transferring control policies for arbitrary tasks from a human to a robot. Using interactive demonstration via teloperation as our transfer scenario, we ca...