Port "add -R explanation in README (and mention that generally it's not needed)"

Ported from cjlin1/liblinear@c3af75a

Author: bwaldvogel

README.md

@@ -261,7 +261,15 @@ where
 
     Q is a matrix with Q_ij = x_i^T x_j.
 
-If `bias >= 0`, w becomes `[w; w_{n+1}]` and x becomes `[x; bias]`.
+If `bias >= 0`, w becomes `[w; w_{n+1}]` and x becomes `[x; bias]`.  For
+example, L2-regularized logistic regression (-s 0) becomes
+
+    min_w w^Tw/2 + (w_{n+1})^2/2 + C \sum log(1 + exp(-y_i [w; w_{n+1}]^T[x_i; bias]))
+
+Some may prefer not having `(w_{n+1})^2/2` (i.e., bias variable not
+regularized). For primal solvers (-s 0, 2, 5, 6, 11), we provide an
+option -R to remove `(w_{n+1})^2/2`. However, -R is generally not needed
+as for most data with/without `(w_{n+1})^2/2` give similar performances.
 
 The primal-dual relationship implies that -s 1 and -s 2 give the same
 model, -s 0 and -s 7 give the same, and -s 11 and -s 12 give the same.