Merge branch 'master' of https://github.com/CompPhysics/MachineLearning

2020-11-26 14:48:34 +01:00
parent 249d8bf053 637b2c1eae
commit c0fbe06872
3 changed files with 152 additions and 17 deletions
@@ -57,9 +57,16 @@ 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
+<<<<<<< HEAD
+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 obtain a separation between the
+classes which is almost linear.
+=======
 here generated by the code below (see also Figures 12.2 and 12.3 of "Hastie et al.":"https://www.springer.com/gp/book/9780387848570"), we can achieve a better separation by introducing more complex
 basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to 
 obtain a separation between the classes which is almost linear. 
+>>>>>>> origin/master

 The change of basis, from $x\rightarrow z=\phi(x)$ leads to the same type of equations to be solved, except that
 we need to introduce, for example, a polynomial transformation to a two-dimensional training set.