Adaptive and Self-Confident On-Line Learning Algorithms

Adaptive and Self-Confident On-Line Learning Algorithms

Most of the performance bounds for on-line learning algorithms are proven assuming a constant learning rate. To optimize these bounds, the learning rate must be tuned based on quantities that are generally unknown, as they depend on the whole sequence of examples. In this paper we show that essentially the same optimized bounds can be obtained when the algorithms adaptively tune their learning ...

Adaptive and Self-Con dent On-Line Learning Algorithms

Adaptive On - line Learning in

An adaptive on-line algorithm extending the learning of learning idea is proposed and theoretically motivated. Relying only on gradient ow information it can be applied to learning continuous functions or distributions, even when no explicit loss function is given and the Hessian is not available. Its eeciency is demonstrated for a non-stationary blind separation task of acoustic signals.

Intervention Strategies to Increase Self-efficacy and Self-regulation in Adaptive On-Line Learning

This research outline refers to the validation of interventional strategies to increase the learner’s motivation and self-efficacy in an on-line learning environment. Previous work in this area is mainly based on Keller’s ARCS model of instructional design and this study argues for an approach based on Bandura’s Social Cognitive Theory – especially the aspects of self-efficacy and self-regulati...

Adaptive Analysis of On-line Algorithms

On-line algorithms are usually analyzed using competitive analysis, in which the performance of on-line algorithm on a sequence is normalized by the performance of the optimal off-line algorithm on that sequence. In this paper we introduce adaptive/cooperative analysis as an alternative general framework for the analysis of on-line algorithms. This model gives promising results when applied to ...