Parsimonious classification via generalised linear mixed models
ثبت نشده
چکیده
We devise a classification algorithm based on generalised linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the resulting classifiers are generally interpretable and parsimonious.
منابع مشابه
Generalised linear mixed model analysis via sequential Monte Carlo sampling
We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chain Monte Carlo (MCMC). The Sequential Monte Carlo sampler (SMC) is a new and general...
متن کاملKernel discriminant analysis and clustering with parsimonious Gaussian process models
This work presents a family of parsimonious Gaussian process models which allow to build, from a finite sample, a model-based classifier in an infinite dimensional space. The proposed parsimonious models are obtained by constraining the eigendecomposition of the Gaussian processes modeling each class. This allows in particular to use non-linear mapping functions which project the observations i...
متن کاملSolving Single Machine Sequencing to Minimize Maximum Lateness Problem Using Mixed Integer Programming
Despite existing various integer programming for sequencing problems, there is not enoughinformation about practical values of the models. This paper considers the problem of minimizing maximumlateness with release dates and presents four different mixed integer programming (MIP) models to solve thisproblem. These models have been formulated for the classical single machine problem, namely sequ...
متن کامل