added steepest descent boosting

doc/src/DecisionTrees/DecisionTrees.do.txt

@@ -1938,7 +1938,7 @@ plt.show()
 
 
 !split
-===== Gradient boosting: Basics =====
+===== Gradient boosting: Basics with Steepest Descent =====
 
 Gradient boosting is again a similar technique to Adaptive boosting,
 it combines so-called weak classifiers or regressors into a strong
@@ -1948,7 +1948,20 @@ In order to understand the method, let us illustrate its basics by
 bringing back the essential steps in linear regression, where our cost
 function was the least squares function.
 
-See discussion during lecture November 8.
+!split
+===== The Squared-Error again! Steepest Descent =====
+
+We start again with our cost function ${\cal C}(\bm{y}m\bm{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i))$ where we want to minimize
+This means that for every iteration, we need to optimize
+
+!bt
+\[
+(\hat{\bm{f}})  \mathrm{argmin}_{\bm{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2.
+\]
+!et
+
+
+
 
 !split
 ===== Gradient Boosting, algorithm =====