We address the issue of learning multi-layered perceptrons (MLPs) in a discriminative, inductive, multiclass, parametric, and semi-supervised fashion. We introduce a novel objective function that, when optimized, simultaneously encourages 1) accuracy on the labeled points, 2) respect for an underlying graph-represented manifold on all points, 3) smoothness via an entropic regularizer of the cla...

1. My main goal was to develop statistical approaches to the study of community dynamics in the presence of large numbers of species. In previous work, I used semi-parametric methods to build up a picture of community dynamics aggregated into a small number of categories. The basic idea was to predict the composition of a community one time unit into the future, given its current composition, u...

The common approach to SNP genotyping is to use (model-based) clustering per individual SNP, on a set of arrays. Genotyping all SNPs on a single array is much more attractive, in terms of flexibility, stability and applicability, when developing new chips. A new semi-parametric method, named SCALA, is proposed. It is based on a mixture model using semi-parametric log-concave densities. Instead ...

Apart from kernel estimators, there have been quite a few different approaches of “generalized splines” for density estimation. In the present paper,Maximum Penalized Likelihood (mpl) approaches are reviewed. In conclusion, penalizing the log density seems most promising. In my “wp” approach for semi-parametric density estimation, a novel roughness penalty is introduced. It penalizes a relative...

Non-parametric and semi-parametric analysis of panel count data have recently been an active research topic in statistical literature. Maximum likelihood method based on non-homogeneous Poisson process has been proved an efficient inference procedure for such analysis. However, computing the nonand semi-parametric maximum likelihood estimates (MLE) can be very intensive numerically. In this man...

We present a semi–parametric approach to evaluate the reliability of rules obtained from a rough set information system by replacing strict determinacy by predicting a random variable which is a mixture of latent probabilities obtained from repeated measurements of the decision variable. It is demonstrated that the algorithm may be successfully used for unsupervised learning.

We consider nonlinear models with an independent variable that is measured with error. The measurement error can be correlated with the true value, i.e. the measurement error is allowed to be nonclassical. We show that we can use a control variate estimator to estimate the parameters of interest. If we are prepared to make an assumption of the joint distribution of the first-stage and measureme...

survival analysis is a set of methods used for analysis of the data which exist until the occurrence of an event. this study aimed to compare the results of the use of the semi-parametric cox model with parametric models to determine the factors influencing the length of stay of patients in the inpatient units of women hospital in tehran, iran. in this historical cohort study all 3421 charts of...

This article proposes a semi-parametric stochastic frontier model (SPSF) in which components of the technology and of technical efficiency are represented using semi-parametric methods and estimated in a Bayesian framework. The approach is illustrated in an application to US farm data. The analysis shows important scale economies for small and medium herds and constant return to scale for large...