Due to the increase in the amount of data gathered every day in the real world problems (e.g., bioinformatics), there is a need for inductive learning algorithms that can incrementally process large amounts of data that is being accumulated over time in physically distributed, autonomous data repositories. In the incremental setting, the learner gradually refines a hypothesis (or a set of hypot...

Regional cerebral blood flow was examined during multiple-trial learning in healthy volunteers. On the basis that incremental learning from trial to trial is severely impaired in neuropsychological studies of patients with medial temporal lesions, we predicted that medial temporal activation might be particularly associated with incremental gains in learning. On the other hand, we predicted tha...

a r t i c l e i n f o We introduce an innovative incremental learner called incremental import vector machines (I 2 VM). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machin...

We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic data and knowledge sources. We introduce a family of learning operators for precise specification of some existing solutions and to facilitate the design and analysis of new algorithms for this class of problems. We s...

The online learning of deep neural networks is an interesting problem of machine learning because, for example, major IT companies want to manage the information of the massive data uploaded on the web daily, and this technology can contribute to the next generation of lifelong learning. We aim to train deep models from new data that consists of new classes, distributions, and tasks at minimal ...

This paper discusses the problems of developing adaptive self-explaining interfaces for advanced World-Wide Web (WWW) applications. Two kinds of adaptation are considered: incremental learning and incremental interfaces. The key problem for these kinds of adaptation is to decide which interface features should be explained or enabled next. We analyze possible ways to implement incremental learn...

MailCat is an intelligent assistant that helps users organize their e-mail into folders. MailCat uses a text classiier to predict where each new message is likely to be led by the user and provides shortcut buttons to quickly le messages into one of its predicted folders. MailCat operates in a dynamic environment in which it must adapt to the user's continually changing mail-ling habits. MailCa...