From c83df166d446c49be1417817f06a344bbaf5f564 Mon Sep 17 00:00:00 2001 From: Benjamin Auder Date: Mon, 10 Dec 2018 17:50:11 +0100 Subject: [PATCH] Vignette... TODO --- vignettes/report.Rmd | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/vignettes/report.Rmd b/vignettes/report.Rmd index a67223b..ab00501 100644 --- a/vignettes/report.Rmd +++ b/vignettes/report.Rmd @@ -1,5 +1,5 @@ --- -title: morpheus........... +title: Use morpheus package output: pdf_document: @@ -13,7 +13,8 @@ knitr::opts_chunk$set(echo = TRUE, include = TRUE, out.width = "100%", fig.align = "center") ``` -0) Tell that we try to learn classification parameters in a non-EM way, using algebric manipulations. +## Introduction + *morpheus* is a contributed R package which attempts to find the parameters of a mixture of logistic classifiers. When the data under study come from several groups that have different characteristics, using mixture models is a very popular way to handle heterogeneity. @@ -22,11 +23,13 @@ Thus, many algorithms were developed to deal with various mixtures models. Most However, one problem of such methods is that they can converge to local maxima, so several starting points must be explored. Recently, spectral methods were developed to bypass EM algorithms and they were proved able to recover the directions of the regression parameter -in models with known link function and random covariates (see [9]). +in models with known link function and random covariates (see [XX]). Our package extends such moment methods using least squares to get estimators of the whole parameters (with theoretical garantees, see [XX]). Currently it can handle only binary output $-$ which is a common case. -1) Model. +## Model + + TODO: retrouver mon texte initial + article. -- 2.44.0