IntervalRegressionUnregularized

passed to <code><a rd-options="" href="/link/IntervalRegressionRegularized?package=penaltyLearning&version=2020.5.13" data-mini-rdoc="penaltyLearning::IntervalRegressionRegularized">IntervalRegressionRegularized</a></code>.

&#8230;

Use <code><a rd-options="" href="/link/IntervalRegressionRegularized?package=penaltyLearning&version=2020.5.13" data-mini-rdoc="penaltyLearning::IntervalRegressionRegularized">IntervalRegressionRegularized</a></code> with initial.regularization=0
and factor.regularization=NULL, meaning fit one un-regularized
interval regression model.

Implementations of algorithms from
Learning Sparse Penalties for Change-point Detection
using Max Margin Interval Regression, by
Hocking, Rigaill, Vert, Bach
<http://proceedings.mlr.press/v28/hocking13.html>
published in proceedings of ICML2013.

Toby Hocking

penaltyLearning

Penalty Learning

IntervalRegressionUnregularized function

passed to <code><a rd-options='' href='IntervalRegressionRegularized'>IntervalRegressionRegularized</a></code>.

Use <code><a rd-options='' href='IntervalRegressionRegularized'>IntervalRegressionRegularized</a></code> with initial.regularization=0
and factor.regularization=NULL, meaning fit one un-regularized
interval regression model.

IntervalRegressionUnregularized: IntervalRegressionUnregularized

Description

Usage

Arguments

Value