search for: rulelearn

Displaying 5 results from an estimated 5 matches for "rulelearn".

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New Webinar Series: The Evolution of Regression From Classical Linear Regression to Modern Ensembles (Hands-on Component)
2013 Feb 06
0
New Webinar Series: The Evolution of Regression From Classical Linear Regression to Modern Ensembles (Hands-on Component)
...ted student: apply these methods to your own data (optional) Part 3: March 29 - Regression methods discussed *Part 1 is a recommended pre-requisite * Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF * Ensemble Post-Processing: ISLE; RuleLearner Part 4: April 12 - 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) [[...
Reminder: Webinar Series-- The Evolution of Regression From Classical Linear Regression to Modern Ensembles (Hands-on Component)
2013 Feb 25
0
Reminder: Webinar Series-- The Evolution of Regression From Classical Linear Regression to Modern Ensembles (Hands-on Component)
...ted student: apply these methods to your own data (optional) Part 3: March 29 - Regression methods discussed *Part 1 is a recommended pre-requisite * Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF * Ensemble Post-Processing: ISLE; RuleLearner Part 4: April 12 - 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) [[...
Tomorrow: The Evolution of Regression from Classical Linear Regression to Modern Ensembles (hands-on)
2013 Mar 14
0
Tomorrow: The Evolution of Regression from Classical Linear Regression to Modern Ensembles (hands-on)
...regression-modeling-part-1 > > Part 3: March 29, 10-11am PST - Regression methods discussed > *Part 1 is a recommended pre-requisite > > Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF > Ensemble Post-Processing: ISLE; RuleLearner > Part 4: 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 ow...
Hands-on Webinar: Advances in Regression: Modern Ensemble and Data Mining Approaches (no charge)
2013 Mar 20
0
Hands-on Webinar: Advances in Regression: Modern Ensemble and Data Mining Approaches (no charge)
.... Part 3: March 29, 10-11am PST - Regression methods discussed: * Nonlinear Ensemble Approaches: o TreeNet Gradient Boosting o Random Forests o Gradient Boosting incorporating Random Forests * Ensemble Post-Processing: o ISLE Importance Sampled Learning Ensembles o RuleLearner rule based learning ensembles Part 4: 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 the...
Hands-on Webinar Series (no charge) The Evolution of Regression from Classical Linear Regression to Modern Ensembles
2013 Mar 11
0
Hands-on Webinar Series (no charge) The Evolution of Regression from Classical Linear Regression to Modern Ensembles
...805-the-evolution-of-regression-modeling-part-1 Part 3: March 29, 10-11am PST - Regression methods discussed *Part 1 is a recommended pre-requisite * Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF * Ensemble Post-Processing: ISLE; RuleLearner Part 4: 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 (op...