A useful strategy to deal with complex classification scenarios is the “divide and conquer” approach. The mixture of experts (MOE) technique makes use of this strategy by joinly training a set of classifiers, or experts, that are specialized in different regions of the input space. A global model, or gate function, complements the experts by learning a function that weights their relevance in d...

Over the years, police have become a central component of international peace- and statebuilding operations. However, predominantly trained socialized as members domestic service, officers enter global arena only – if ever temporarily: their deployment is often merely an interlude to regular service. Pre-deployment training for missions therefore deemed vital success. When abroad, oftentimes fi...

The Mixture of Experts, as it was originally formulated, is a static algorithm in the sense that the output of the network, and parameter updates during training, are completely independent from one time step to the next. This independence creates difficulties when the model is applied to time series prediction. We address this by adding memory to the Mixture of Experts. A Gaussian assumption o...

in this research we have presented a local model for implementing systems engineering activities in optimized acquisition of electronic systems in electronic high-tech industrial. in this regard, after reviewing the literature and the use of documents, articles and latin books, we have collected system acquisition life cycle models from different resources. after considering the criteria of the...

We propose a scalable nonparametric Bayesian regression model based on a mixture of Gaussian process (GP) experts and the inducing points formalism underpinning sparse GP approximations. Each expert is augmented with a set of inducing points, and the allocation of data points to experts is defined probabilistically based on their proximity to the experts. This allocation mechanism enables a fas...