In this paper we examine on-line learning with statistical framework. Firstly we study the cases with xed and annealed learning rate. It can be shown that on-line learning with 1=t annealed learning rate minimizes the generalization error with the same rate as batch learning in the asymp-totic regime, that is, on-line learning can be as eeective as batch learning asymptotically. Using these ana...

Stochastic gradient descent is a general algorithm that includes LMS, on-line backpropagation, and adaptive k-means clustering as special cases. The standard choices of the learning rate (both adap-tive and xed functions of time) often perform quite poorly. In contrast, our recently proposed class of \search then converge" (STC) learning rate schedules (Darken and Moody, 1990b, 1991) display th...

Hebbian learning rules are generally formulated as static rules. Under changing condition (e.g. neuromodulation, input statistics) most rules are sensitive to parameters. In particular, recent work has focused on two different formulations of spike-timing-dependent plasticity rules. Additive STDP [1] is remarkably versatile but also very fragile, whereas multiplicative STDP [2, 3] is more robus...

We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. develop game theoretic model of interaction rate adaptive load balancing operator under function-oriented pay-as-you-go pricing model. show that perfect information, strategic can be formulated as generalized Nash equilibrium problem, use variatio...

In transfer learning, we wish to make inference about a target population when have access data both from the distribution itself, and different but related source distribution. We introduce flexible framework for learning in context of binary classification, allowing covariate-dependent relationships between distributions that are not required preserve Bayes decision boundary. Our main contrib...

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inp...

The presence of near-end interferences and echo path changes make it essential for an adaptive filter to vary its learning rate according to corresponding conditions. In this paper, a robust step-size control algorithm which is based on the optimization of the square of the bin-wise a posteriori error is proposed. To prevent the adaptive filter from diverging in the presence of interferences, c...