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March 2013

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Wed, 27 Mar 2013 17:38:59 -0400
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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
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Lisa Solomon <[log in to unmask]>
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Modern Ensemble and Data Mining Approaches
 Part of the series: The Evolution of Regression from Classical Linear Regression to Modern Ensembles 
 Hands-on, No charge 

Registration Link: https://www1.gotomeeting.com/register/500959705
 All registrants will automatically receive access to recordings of earlier sessions which covered: Classical Regression, Logistic Regression, Regularized Regression, Nonlinear Regression, MARS Regression Splines

Course Abstract: Overcoming Linear Regression Limitations
Regression is one of the most popular modeling methods, but the classical
approach has significant problems. This webinar series addresses these
problems. Are you working with larger datasets? Is your data challenging? Does your data include missing values, nonlinear relationships, local patterns and interactions? This webinar series is for you!

In our March 29th session we will focus on modern ensemble
and data mining approaches. These methods dramatically improve the
performance of weak learners such as regression trees. The techniques
discussed here enhance the performance of regression trees
considerably. These methods inherit the good features of trees (variable
selection, missing data, mixed predictors) and improve on the weak features
such as prediction performance.

March 29, 10-11am PST - Regression methods discussed:
Nonlinear Ensemble Approaches: 
 TreeNet Gradient Boosting 
 Random Forests 
 Gradient Boosting incorporating Random Forests 
Ensemble Post-Processing: 
 ISLE Importance Sampled Learning Ensembles 
 RuleLearner rule based learning ensembles 

April 12,10-11am PST - Hands-on demonstration of concepts discussed in Part 3
 Step-by-step demonstration 
 Datasets and software available for download 
 Instructions for reproducing demo at your leisure 
 For the dedicated student: apply these methods to your own data (optional)

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