small typos

doc/pub/week47/html/._week47-bs003.html

@@ -357,7 +357,7 @@ MathJax.Hub.Config({
 the mathematical description of so-called hyperplanes. Let us start
 with a two-dimensional case. This will also allow us to introduce our
 first SVM examples. These will be tailored to the case of two specific
-classes, as displayed in the figure here based on the usage of the petal data.
+classes, as displayed in the figure here  based on the usage of the petal data.
 </p>
 
 <p>We assume here that our data set can be well separated into two

doc/pub/week47/html/._week47-bs017.html

@@ -356,7 +356,10 @@ MathJax.Hub.Config({
 <p>Till now, the margin is strictly defined by the support vectors. This defines what is called a hard classifier, that is the margins are well defined.</p>
 
 <p>Suppose now that classes overlap in feature space, as shown in the
-figure here. One way to deal with this problem before we define the
+figure in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_self">handwritten notes</a> for Thursday November 25.
+</p>
+
+<p>One way to deal with this problem before we define the
 so-called <b>kernel approach</b>, is to allow a kind of slack in the sense
 that we allow some points to be on the wrong side of the margin.
 </p>

doc/pub/week47/html/._week47-bs019.html

@@ -362,8 +362,9 @@ wavelets, splines etc.
 </p>
 
 <p>If our feature space is not easy to separate, as shown in the figure
-here, we can achieve a better separation by introducing more complex
-basis functions. The ideal would be, as shown in the next figure, to, via a specific transformation to 
+in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_self">handwritten notes</a> for Thursday November 25.
+, we can achieve a better separation by introducing more complex
+basis functions. The ideal would be to, via a specific transformation to 
 obtain a separation between the classes which is almost linear. 
 </p>
 

doc/pub/week47/html/week47-reveal.html

@@ -251,7 +251,7 @@ unlikely that we can separate classes easily by say straight lines.
 the mathematical description of so-called hyperplanes. Let us start
 with a two-dimensional case. This will also allow us to introduce our
 first SVM examples. These will be tailored to the case of two specific
-classes, as displayed in the figure here based on the usage of the petal data.
+classes, as displayed in the figure here  based on the usage of the petal data.
 </p>
 
 <p>We assume here that our data set can be well separated into two
@@ -845,7 +845,10 @@ $$
 <p>Till now, the margin is strictly defined by the support vectors. This defines what is called a hard classifier, that is the margins are well defined.</p>
 
 <p>Suppose now that classes overlap in feature space, as shown in the
-figure here. One way to deal with this problem before we define the
+figure in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank">handwritten notes</a> for Thursday November 25.
+</p>
+
+<p>One way to deal with this problem before we define the
 so-called <b>kernel approach</b>, is to allow a kind of slack in the sense
 that we allow some points to be on the wrong side of the margin.
 </p>
@@ -959,8 +962,9 @@ wavelets, splines etc.
 </p>
 
 <p>If our feature space is not easy to separate, as shown in the figure
-here, we can achieve a better separation by introducing more complex
-basis functions. The ideal would be, as shown in the next figure, to, via a specific transformation to 
+in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank">handwritten notes</a> for Thursday November 25.
+, we can achieve a better separation by introducing more complex
+basis functions. The ideal would be to, via a specific transformation to 
 obtain a separation between the classes which is almost linear. 
 </p>
 

doc/pub/week47/html/week47-solarized.html

@@ -350,7 +350,7 @@ unlikely that we can separate classes easily by say straight lines.
 the mathematical description of so-called hyperplanes. Let us start
 with a two-dimensional case. This will also allow us to introduce our
 first SVM examples. These will be tailored to the case of two specific
-classes, as displayed in the figure here based on the usage of the petal data.
+classes, as displayed in the figure here  based on the usage of the petal data.
 </p>
 
 <p>We assume here that our data set can be well separated into two
@@ -849,7 +849,10 @@ $$
 <p>Till now, the margin is strictly defined by the support vectors. This defines what is called a hard classifier, that is the margins are well defined.</p>
 
 <p>Suppose now that classes overlap in feature space, as shown in the
-figure here. One way to deal with this problem before we define the
+figure in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank">handwritten notes</a> for Thursday November 25.
+</p>
+
+<p>One way to deal with this problem before we define the
 so-called <b>kernel approach</b>, is to allow a kind of slack in the sense
 that we allow some points to be on the wrong side of the margin.
 </p>
@@ -940,8 +943,9 @@ wavelets, splines etc.
 </p>
 
 <p>If our feature space is not easy to separate, as shown in the figure
-here, we can achieve a better separation by introducing more complex
-basis functions. The ideal would be, as shown in the next figure, to, via a specific transformation to 
+in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank">handwritten notes</a> for Thursday November 25.
+, we can achieve a better separation by introducing more complex
+basis functions. The ideal would be to, via a specific transformation to 
 obtain a separation between the classes which is almost linear. 
 </p>
 

doc/pub/week47/html/week47.html

@@ -427,7 +427,7 @@ unlikely that we can separate classes easily by say straight lines.
 the mathematical description of so-called hyperplanes. Let us start
 with a two-dimensional case. This will also allow us to introduce our
 first SVM examples. These will be tailored to the case of two specific
-classes, as displayed in the figure here based on the usage of the petal data.
+classes, as displayed in the figure here  based on the usage of the petal data.
 </p>
 
 <p>We assume here that our data set can be well separated into two
@@ -926,7 +926,10 @@ $$
 <p>Till now, the margin is strictly defined by the support vectors. This defines what is called a hard classifier, that is the margins are well defined.</p>
 
 <p>Suppose now that classes overlap in feature space, as shown in the
-figure here. One way to deal with this problem before we define the
+figure in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank">handwritten notes</a> for Thursday November 25.
+</p>
+
+<p>One way to deal with this problem before we define the
 so-called <b>kernel approach</b>, is to allow a kind of slack in the sense
 that we allow some points to be on the wrong side of the margin.
 </p>
@@ -1017,8 +1020,9 @@ wavelets, splines etc.
 </p>
 
 <p>If our feature space is not easy to separate, as shown in the figure
-here, we can achieve a better separation by introducing more complex
-basis functions. The ideal would be, as shown in the next figure, to, via a specific transformation to 
+in the <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" target="_blank">handwritten notes</a> for Thursday November 25.
+, we can achieve a better separation by introducing more complex
+basis functions. The ideal would be to, via a specific transformation to 
 obtain a separation between the classes which is almost linear. 
 </p>
 

doc/src/week47/week47.do.txt

@@ -52,7 +52,7 @@ The theory behind support vector machines (SVM hereafter) is based on
 the mathematical description of so-called hyperplanes. Let us start
 with a two-dimensional case. This will also allow us to introduce our
 first SVM examples. These will be tailored to the case of two specific
-classes, as displayed in the figure here based on the usage of the petal data.
+classes, as displayed in the figure here  based on the usage of the petal data.
 
 We assume here that our data set can be well separated into two
 domains, where a straight line does the job in the separating the two
@@ -561,7 +561,10 @@ Below we discuss how to find the optimal values of $\lambda_i$. Before we procee
 Till now, the margin is strictly defined by the support vectors. This defines what is called a hard classifier, that is the margins are well defined.
 
 Suppose now that classes overlap in feature space, as shown in the
-figure here. One way to deal with this problem before we define the
+figure in the "handwritten notes":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" for Thursday November 25.
+
+
+One way to deal with this problem before we define the
 so-called _kernel approach_, is to allow a kind of slack in the sense
 that we allow some points to be on the wrong side of the margin.
 
@@ -658,8 +661,9 @@ space using other basis expansions such as higher-order polynomials,
 wavelets, splines etc.
 
 If our feature space is not easy to separate, as shown in the figure
-here, we can achieve a better separation by introducing more complex
-basis functions. The ideal would be, as shown in the next figure, to, via a specific transformation to 
+in the "handwritten notes":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/HandWrittenNotes/2021" for Thursday November 25.
+, we can achieve a better separation by introducing more complex
+basis functions. The ideal would be to, via a specific transformation to 
 obtain a separation between the classes which is almost linear. 
 
 The change of basis, from $x\rightarrow z=\phi(x)$ leads to the same type of equations to be solved, except that