diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html
index fd1d57876..d3af51f30 100644
--- a/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html
+++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html
@@ -235,7 +235,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 3, 2019</h4></center> <!-- date -->
+<center><h4>Nov 4, 2019</h4></center> <!-- date -->
 <br>
 <p>
 
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-bs.html b/doc/pub/DecisionTrees/html/DecisionTrees-bs.html
index fd1d57876..d3af51f30 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-bs.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-bs.html
@@ -235,7 +235,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 3, 2019</h4></center> <!-- date -->
+<center><h4>Nov 4, 2019</h4></center> <!-- date -->
 <br>
 <p>
 
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html b/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
index 69d57ac10..1f48a1ff5 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Nov 3, 2019</h4></center> <!-- date -->
+<center><h4>Nov 4, 2019</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -1847,6 +1847,17 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
 
 <p>
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
+The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
+
+<ol>
+<p><li> We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).</li>
+<p><li> We rewrite the misclassification error as</li> 
+</ol>
+<p>&nbsp;<br>
+$$
+\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
+$$
+<p>&nbsp;<br>
 </section>
 
 
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html b/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
index cffd36929..ee7f98b36 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
@@ -175,7 +175,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 3, 2019</h4></center> <!-- date -->
+<center><h4>Nov 4, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1823,6 +1823,16 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
 
 <p>
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
+The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
+
+<ol>
+<li> We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).</li>
+<li> We rewrite the misclassification error as</li> 
+</ol>
+
+$$
+\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
+$$
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
diff --git a/doc/pub/DecisionTrees/html/DecisionTrees.html b/doc/pub/DecisionTrees/html/DecisionTrees.html
index c55979d56..50175bd87 100644
--- a/doc/pub/DecisionTrees/html/DecisionTrees.html
+++ b/doc/pub/DecisionTrees/html/DecisionTrees.html
@@ -180,7 +180,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Nov 3, 2019</h4></center> <!-- date -->
+<center><h4>Nov 4, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1828,6 +1828,16 @@ where the function \( I() \) is one if we misclassify and zero if we classify co
 
 <p>
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
+The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
+
+<ol>
+<li> We start by initializing all weights to \( w_i = 1/n \), with \( i=0,1,2,\dots n-1 \). It is to see then that \( \sum_{i=0}^{n-1}w_i = 1 \).</li>
+<li> We rewrite the misclassification error as</li> 
+</ol>
+
+$$
+\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\boldsymbol{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
+$$
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
diff --git a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
index 711df9623..e84a1c377 100644
--- a/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
+++ b/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
@@ -10,7 +10,7 @@
     "<!-- Author: -->  \n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Nov 3, 2019**\n",
+    "Date: **Nov 4, 2019**\n",
     "\n",
     "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
@@ -1875,9 +1875,25 @@
     "## Basic Steps of AdaBoost\n",
     "\n",
     "With the above definitions we are now ready to set up the algorithm for AdaBoost.\n",
+    "The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.\n",
+    "1. We start by initializing all weights to $w_i = 1/n$, with $i=0,1,2,\\dots n-1$. It is to see then that $\\sum_{i=0}^{n-1}w_i = 1$.\n",
     "\n",
-    "\n",
-    "\n",
+    "2. We rewrite the misclassification error as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "\\mathrm{err}=\\frac{\\sum_{i=0}^{n-1}w_iI(y_i\\ne G(\\boldsymbol{X}_{i*})}{\\sum_{i=0}^{n-1}w_i},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## AdaBoost Examples"
    ]
   },
diff --git a/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz b/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz
index 2a0d1b88a..f87e98931 100644
Binary files a/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz and b/doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz differ
diff --git a/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf b/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf
index e88f2302b..53ae56b4b 100644
Binary files a/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf and b/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf differ
diff --git a/doc/src/DecisionTrees/DecisionTrees.do.txt b/doc/src/DecisionTrees/DecisionTrees.do.txt
index 05ddd3d3f..787ba6bb8 100644
--- a/doc/src/DecisionTrees/DecisionTrees.do.txt
+++ b/doc/src/DecisionTrees/DecisionTrees.do.txt
@@ -1501,6 +1501,14 @@ where the function $I()$ is one if we misclassify and zero if we classify correc
 ===== Basic Steps of AdaBoost =====
 
 With the above definitions we are now ready to set up the algorithm for AdaBoost.
+The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.
+o We start by initializing all weights to $w_i = 1/n$, with $i=0,1,2,\dots n-1$. It is to see then that $\sum_{i=0}^{n-1}w_i = 1$.
+o We rewrite the misclassification error as 
+!bt
+\[
+\mathrm{err}=\frac{\sum_{i=0}^{n-1}w_iI(y_i\ne G(\bm{X}_{i*})}{\sum_{i=0}^{n-1}w_i},
+\]
+!et