Soft sensors are widely constructed in process industry to realize process monitoring, quality
prediction, and many other important applications. With the development of hardware and …

The deep learning paradigm tackles problems on which shallow architectures (eg SVM) are
affected by the curse of dimensionality. As part of a two-stage learning scheme involving …

Advances in diffuse reflectance infra-red spec-cryoscopy measurements have made it
possible to estimate number of functional properties of soil inexpensively and accurately …

Human activity recognition (HAR) is highly relevant to many real-world domains like safety,
security, and in particular healthcare. The current machine learning technology of HAR is …

In this paper, a hybrid approach to combine conditional restricted Boltzmann machines
(CRBM) and echo state networks (ESN) for binary time series prediction is proposed. Both …

Conditional restricted Boltzmann machines are undirected stochastic neural networks with a
layer of input and output units connected bipartitely to a layer of hidden units. These …

In multi-response regression, pursuit of two different types of structures is essential to battle
the curse of dimensionality. In this paper, we seek a sparsest decomposition representation …

Purpose Technical advancements have been part of modern medical solutions as they
promote better surgical alternatives that serve to the benefit of patients. Particularly with …

In this paper, we propose to apply deep belief networks (DBN) to predict potential
operational disruptions caused by rail vehicle door systems. DBN are a powerful algorithm …

Contact switches and touch screens are the state of the art for recording pigeons' pecking
behavior. Recording other behavior, however, requires a different sensor for each behavior …