Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Date 9-11 Dec. 2015
Machine (Statistical) learning methods are used for predicting the delivery times of the packages transmitted through the data network (DN). The statistical model of the DN is proposed, this model allows predicting the delivery times depending on a state of the DN (network load) and the statistical dependences between the delivery times of different transmitted packages. For constructing this model, various statistical methods (forecasting, dimensionality reduction) are applied to the data which are the results of computational experiments performed with detailed simulation model of the DN. The constructed model simulates the processes of package transmission over the DN. Motivation for a construction of such model is a need to create Monte Carlo network simulators to imitate the delivery times of transmitted packages, such simulators can be used in modeling of Information and Control Systems whose objects communicate with each other through the DN.
A new geometrically motivated method is proposed for solving the non-linear regression task consisting in constructing a predictive function which estimates an unknown smooth mapping f from q-dimensional inputs to m-dimensional outputs based on a given 'input-output' training pairs. The unknown mapping f determines q-dimensional Regression manifold M(f) consisting of all the (q+m)-dimensional 'input-output' vectors. The manifold is covered by a single chart, the training data set determines a manifold-valued sample from this manifold. Modern Manifold Learning technique is used for constructing the certain estimator M* of the Regression manifold from the sample which accurately approximates the Regression manifold. The proposed method called Manifold Learning Regression (MLR) finds the predictive function fMLR to ensure an equality M(fMLR) = M*. The MLR estimates also the m×q Jacobian matrix of the mapping f.