Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are linear models, in this paper we investigate learning nonlinear tree models. ...

Although, Single-Sided Linear Induction Motor (SLIM) utilization has increased in railway applications due to their numerous advantages in comparison to Rotational Induction Motors (RIM), there are some sophistication in their mathematical models and electrical drive. This paper focuses on the problems of SLIM modeling, with assuming end-effect on the basis of Field Oriented Control (FOC) as a ...

This paper proposes a Time Delay nonlinear dynamic model of HIV-1 (Human Immunodeficiency Virus type 1), introducing the drug consumption efficiencies as the controlling input for the model. The paper also represents the fuzzy T-S representation and the corresponding Fuzzy T-S controller. The controller parameters are tuned using LMIs (Linear Matrix Inequalities). The main focus is on the stabi...

this paper considers the search problem, introduced by srivastava cite{sr}. this is a model discrimination problem. in the context of search linear models, discrimination ability of search designs has been studied by several researchers. some criteria have been developed to measure this capability, however, they are restricted in a sense of being able to work for searching only one possible non...

We study a range of syntactic processing tasks using a general statistical framework that consists of a global linear model, trained by the generalized perceptron together with a generic beamsearch decoder. We apply the framework to word segmentation, joint segmentation and POStagging, dependency parsing, and phrase-structure parsing. Both components of the framework are conceptually and comput...

Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accurac...

neural networks are applicable in identification systems from input-output data. in this report, we analyze thehammerstein-wiener models and identify them. thehammerstein-wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...

geometrically nonlinear governing equations for a plate with linear viscoelastic material are derived. the material model is of boltzmann superposi¬tion principle type. a third-order displacement field is used to model the shear deformation effects. for the solution of the nonlinear governing equations the dynamic relaxation (dr) iterative method together with the finite difference discretizati...

The joint segmentation of multiple series is considered. A mixed linear model is used to account for both covariates and correlations between signals. An estimation algorithm based on EM which involves a new dynamic programming strategy for the segmentation step is proposed. The computational efficiency of this procedure is shown and its performance is assessed through simulation experiments. A...

Inthispapertheauthorsbuildonpriorliteraturetodevelopanadaptiveandtime-varyingmetadataenableddynamictopicmodel(mDTM)andapplyittoalargeWeibodatasetusinganonlineGibbs samplerforparameterestimation.Theirapproachsimultaneouslycapturesthemaximumnumberof inherentdynamicfeaturesofmicroblogstherebysettingitapartfromotheronlinedocumentmining metho...