The gap between CPU speeds and the speed of the technologies providing the data is increasing. As a result, latency and bandwidth to needed data is limited by the performance of the storage devices and the networks that connect them to the CPU. Distributed caching techniques are often used to reduce the penalties associated with such caching; however, such techniques need further development to...

– We study the effect of decomposing a series into multiple components and performing forecasts on each component separately. The focus here is on sales data-most of the series considered display both seasonality and trend. Hence the original series is decomposed into trend, seasonality and an irregular component. Multiple forecasting 'experts' are used to forecast each component series. These ...

There has recently been widespread interest in the use of multiple models for classification and regression in the statistics and neural networks communities. The Hierarchical Mixture of Experts (HME) [1] has been successful in a number of regression problems, yielding significantly faster training through the use of the Expectation Maximisation algorithm. In this paper we extend the HME to cla...

We consider the problem of prediction of stationary time series, using the architecture known as mixtures of experts (MEM). Here we suggest a mixture which blends several autoregressive models. This study focuses on some theoretical foundations of the prediction problem in this context. More precisely, it is demonstrated that this model is a universal approximator, with respect to learning the ...

Personal networks are not always diverse or large enough to reach those with the right information. This problem increases when assembling a group of experts from around the world, something which is a challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel of experts, specifically how to select a group of experts from a huge group of people. We...

We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the maximum number of mistakes of the best expert. We propose a new master strategy that achieves the best known performance for online learning with continuous experts in the mistake bounded model. Our ideas are based on...

Abstract Presidents' unilateral sway over policy is of global concern to scholars, practitioners and the general public. While pending actions provoke media speculation about how much authority presidents have change without legislatures, scholarship has yet systematically measure presidential discretion across areas public policy. This study surveys an interdisciplinary panel using discrete ch...