In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
The need for network stability and reliability has led to the growth of autonomic networks [2] that can provide more stable and more reliable communications via on-line measuremen...
– We examine in this paper the tradeoff between application complexity, network complexity, and network efficiency. We argue that the design of the current Internet reflects a ...
We formulate and evaluate distribution-free statistical process control (SPC) charts for monitoring an autocorrelated process when a training data set is used to estimate the marg...
Joongsup Lee, Christos Alexopoulos, David Goldsman...