Hyperlink DOIs against preferred resolver

tutorials/ensembles-stacking/README.md

@@ -48,7 +48,7 @@ Stacking is a broad class of algorithms that involves training a second-level "m
 ### Some Background
 [Leo Breiman](https://en.wikipedia.org/wiki/Leo_Breiman), known for his work on classification and regression trees and the creator of the Random Forest algorithm, formalized stacking in his 1996 paper, ["Stacked Regressions"](http://statistics.berkeley.edu/sites/default/files/tech-reports/367.pdf).  Although the idea originated with [David Wolpert](https://en.wikipedia.org/wiki/David_Wolpert) in 1992 under the name ["Stacked Generalization"](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.1533), the modern form of stacking that uses internal k-fold cross-validation was Dr. Breiman's contribution.
 
-However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner".  Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art."  The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](http://dx.doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
+However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner".  Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art."  The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](https://doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
 
 
 ### Super Learner Algorithm

tutorials/ensembles-stacking/ensembles-stacking.R

@@ -44,7 +44,7 @@
 #### Some Background
 #[Leo Breiman](https://en.wikipedia.org/wiki/Leo_Breiman), known for his work on classification and regression trees and the creator of the Random Forest algorithm, formalized stacking in his 1996 paper, ["Stacked Regressions"](http://statistics.berkeley.edu/sites/default/files/tech-reports/367.pdf).  Although the idea originated with [David Wolpert](https://en.wikipedia.org/wiki/David_Wolpert) in 1992 under the name ["Stacked Generalization"](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.1533), the modern form of stacking that uses internal k-fold cross-validation was Dr. Breiman's contribution.
 #
-#However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner".  Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art."  The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](http://dx.doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
+#However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner".  Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art."  The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](https://doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
 #
 #
 #### Super Learner Algorithm