small typo before jumping into kernels

doc/src/SupportVMachines/svm.do.txt

@@ -215,7 +215,7 @@ $\vert\vert \hat{w}\vert\vert =1$ subject to the condition
 
 !bt
 \[
-y_i(\hat{w}^T\hat{x}_i+b) \geq M \forall i=1,2,\dots, p. 
+y_i(\hat{w}^T\hat{x}_i+b) \geq M \hspace{0.1cm}\forall i=1,2,\dots, p. 
 \]
 !et
 All points are thus at a signed distance from the decision boundary defined by the line $L$. The parameters $b$ and $w_1$ and $w_2$ define this line. 
@@ -223,20 +223,20 @@ All points are thus at a signed distance from the decision boundary defined by t
 We seek thus the largest value $M$ defined by
 !bt
 \[
-\frac{1}{\vert \vert \hat{w}\vert\vert}y_i(\hat{w}^T\hat{x}_i+b) \geq M \forall i=1,2,\dots, n, 
+\frac{1}{\vert \vert \hat{w}\vert\vert}y_i(\hat{w}^T\hat{x}_i+b) \geq M \hspace{0.1cm}\forall i=1,2,\dots, n, 
 \]
 !et
 or just 
 !bt
 \[
-y_i(\hat{w}^T\hat{x}_i+b) \geq M\vert \vert \hat{w}\vert\vert \forall i. 
+y_i(\hat{w}^T\hat{x}_i+b) \geq M\vert \vert \hat{w}\vert\vert \hspace{0.1cm}\forall i. 
 \]
 !et
 If we scale the equation so that $\vert \vert \hat{w}\vert\vert = 1/M$, we have to find the minimum of 
 $\hat{w}^T\hat{w}=\vert \vert \hat{w}\vert\vert$ (the norm) subject to the condition
 !bt
 \[
-y_i(\hat{w}^T\hat{x}_i+b) \geq 1 \forall i.
+y_i(\hat{w}^T\hat{x}_i+b) \geq 1 \hspace{0.1cm}\forall i.
 \]
 !et
 
@@ -373,7 +373,7 @@ subject to the constraints $\lambda_i\geq 0$ and $\sum_i\lambda_iy_i=0$.
 We must in addition satisfy the "Karush-Kuhn-Tucker":"https://en.wikipedia.org/wiki/Karush%E2%80%93Kuhn%E2%80%93Tucker_conditions" (KKT) condition
 !bt
 \[
-\lambda_i\left[y_i(\hat{w}^T\hat{x}_i+b) -1\right] \forall i.
+\lambda_i\left[y_i(\hat{w}^T\hat{x}_i+b) -1\right] \hspace{0.1cm}\forall i.
 \]
 !et
 o If $\lambda_i > 0$, then $y_i(\hat{w}^T\hat{x}_i+b)=1$ and we say that $x_i$ is on the boundary.
@@ -478,7 +478,7 @@ This has in turn the consequences that we change our optmization problem to find
 subject to 
 !bt
 \[
-y_i(\hat{w}^T\hat{x}_i+b)=1-\xi_i \forall i,
+y_i(\hat{w}^T\hat{x}_i+b)=1-\xi_i \hspace{0.1cm}\forall i,
 \]
 !et
 with the requirement $\xi_i\geq 0$.
@@ -498,7 +498,7 @@ and
 and
 !bt
 \[
-\lambda_i = C-\gamma_i \forall i.
+\lambda_i = C-\gamma_i \hspace{0.1cm}\forall i.
 \]
 !et
 Inserting these constraints into the equation for ${\cal L}$ we obtain the same equation as before
@@ -511,7 +511,7 @@ but now subject to the constraints $\lambda_i\geq 0$, $\sum_i\lambda_iy_i=0$ and
 We must in addition satisfy the Karush-Kuhn-Tucker condition which now reads
 !bt
 \[
-\lambda_i\left[y_i(\hat{w}^T\hat{x}_i+b) -(1-\xi_)\right]=0 \forall i,
+\lambda_i\left[y_i(\hat{w}^T\hat{x}_i+b) -(1-\xi_)\right]=0 \hspace{0.1cm}\forall i,
 \]
 !et
 !bt
@@ -522,7 +522,7 @@ We must in addition satisfy the Karush-Kuhn-Tucker condition which now reads
 and 
 !bt
 \[
-y_i(\hat{w}^T\hat{x}_i+b) -(1-\xi_) \geq 0 \forall i.
+y_i(\hat{w}^T\hat{x}_i+b) -(1-\xi_) \geq 0 \hspace{0.1cm}\forall i.
 \]
 !et