Endowing robots with the capability to learn is an important goal for the robotics research community. One part of this research is focused on learning skills, where usually two learning paradigms are used sequentially. First, a robot learns a motor primitive by demonstration (or imitation). Then, it improves this motor primitive with respect to some externally defined criterion. In this paper,...

Text regression has traditionally been tackled using linear models. Here we present a non-linear method based on a deep convolutional neural network. We show that despite having millions of parameters, this model can be trained on only a thousand documents, resulting in a 40% relative improvement over sparse linear models, the previous state of the art. Further, this method is flexible allowing...

Estimators for differential entropy are proposed. The estimators are based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Simple linear regression is utilized to estimate the values of density function and its second derivative at a point. After estimating the values of the probability density function at each of th...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

introduction: the development of an appropriate model for the quality control of an industrial wastewater treatment system can save the time as well as the cost. this study was performed to determine an appropriate model in order to predict the cod and tkn parameters by bod5 and nh4+ in the meybod industrial estate waste water treatment plant (wwtp). methods: this descriptive – analytical study...

Large-scale Non-linear Regression within the MapReduce Framework By: Ahmed Khademzadeh Thesis Advisor: Philip Chan, Ph.D. Regression models have many applications in real world problems such as finance, epidemiology, environmental science, etc.. Big datasets are everywhere these days, and bigger datasets would help us to construct better models from the data. The issue with big datasets is that...

The paper compares several non-linear regression methods on synthetic data sets generated using standard benchmarks for continuous black-box optimization. For that comparison, we have chosen regression methods that have been used as surrogate models in such optimization: radial basis function networks, Gaussian processes, and random forests. Because the purpose of black-box optimization is freq...

Approximate Bayesian inference on the basis of summary statistics is wellsuited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior densi...

In this paper, we consider using a semiparametric regression approach to modelling non-linear autoregressive time series. Based on a ®nite series approximation to non-parametric components, an adaptive selection procedure for the number of summands in the series approximation is proposed. Meanwhile, a large sample study is detailed and a small sample simulation for the Mackey±Glass system is pr...