Many machine learning methods are given as parameterized optimization problems. Important examples of such parameters are regularizationand kernel hyperparameters. These parameters have to be tuned carefully since the choice of their values can have a significant impact on the statistical performance of the learning methods. In most cases the parameter space does not carry much structure and pa...

An analysis is performed to study the flow and heat transfer characteristics of a non-Newtonian Ostwald-de Waele fluid flow over a non-linearly stretching surface in the presence of internal heat generation/ absorption. The stretching velocity and the prescribed surface temperature are assumed to vary with a powerlaw distance from the slit. Using a similarity transformation the governing equati...

In this research article, the constant proportional Caputo approach of fractional derivative is applied to derive generalized thermal and molecular profiles for flow second grade fluid over a vertical plate. The governing equations prescribed model are reduced dimensionless form then solved temperature, concentration, velocity via Laplace transform. Further graphs field variables sketched param...

Some analytical results concerning PI controller tuning based on integrator plus time delay models are worked out and presented. A method for obtaining PI controller parameters, Kp = α kτ , and, Ti = βτ , which ensures a given prescribed maximum time delay error, dτmax, to time delay, τ , ratio parameter δ = dτmax τ , is presented. The corner stone in this method, is a method product parameter,...