A fast and self-adaptive on-line learning detection system

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

A Very Fast Adaptive Face Detection System

In this article we will make a presentation of a real-time face detector which is robust in difficult conditions (bad camera color calibration and complex background) for embedded systems (mobile phones, PDA’s, laptop computers and game boy-like devices). Our system provides a reliable note qualifying the detection of the faces during the sequence. This note makes it possible to focus the atten...

Design of A Self-Tuning Adaptive Power System Stabilizer

Power system stabilizers (PSSs) must be capable of providing appropriate stabilization signals over abroad range of operating conditions and disturbances. The main idea of this paper is changing aclassic PSS (CPSS) to an adaptive PSS using genetic algorithm. This new genetic algorithm based onadaptive PSS (GAPSS) improves power system damping, considerably. The controller design issue isformula...

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.