Boosted Partial Least-Squares Regression

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'&$% Boosted PLS Regression : S eminaire F enelon 2008-1Boosted Partial Least-SquaresRegressionJean-Fran cois DurandMontpellier II University, FranceE-Mail: site: '&$% Boosted PLS Regression : S eminaire F enelon Machine Learning versus Data Mining The data mining prediction process Partial Least-Squares Boosted by introduction to splines Few words on smoothing splines Regression splines Two sets of basis functions Bivariate Regression splines Least-Squares Splines Penalized PLS Regression What isL2Boosting? Ordinary PLS viewed as aL2Boost algorithm The linear PLS Regression : algorithm and model The building-model stage: choosing M PLS Splines (PLSS): a main effects additive model The PLSS model Choosing the tuning parameters Example 1: Multi-collinearity and outliers, the orange juice data Example 2: The Fisher iris data revisited by PLS boosting MAPLSS to capture interactions The ANOVA type model for main effects and interactions The building-model stage Example 3: Comparison between MAPLSS, MARS and BRUTO on simu-lated data Example 4: Multi-collinearity and bivariate interaction, the chem '&$% Boosted PLS Regression : S eminaire F enelon Learning versus Data miningMachine Learning: machine learning is concerned with the designand development of algorithms and techniques that allow comput-ers to learn.

Boosted PLS Regression: S¶eminaire J.P. F¶enelon 2008-2I. Introduction { Machine Learning versus Data Mining { The data mining prediction process

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