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--- assignments:assignment4 [2016/09/30 11:26]
asa [Submission]
+++ assignments:assignment4 [2016/10/05 11:41]
asa [Part 3: Soft-margin SVM for separable data]
@@ Line 17: / Line 17: @@
   * Consider the following statement:  The set of all key support vectors is unique.  Prove this, or show a counter-example.
-  * Using the definition of key support vectors prove a tighter bound on the leave-one-out cross validation error:
+  * In class we argued that the fraction of examples that are support vectors provide a bound on the leave-one-out error.  Using the definition of key support vectors prove a tighter bound on the leave-one-out cross validation error can be obtained:
 $$
 E_{cv} \leq \frac{\textrm{number of key support vectors}}{N},
@@ Line 27: / Line 27: @@
 Suppose you are given a linearly separable dataset, and you are training the soft-margin SVM, which uses slack variables with the soft-margin constant $C$ set
-with the soft margin constant $C$ set
 to some positive value.
 Consider the following statement:
 Since increasing the $\xi_i$ can only increase the objective of the primal problem (which
-we are trying to minimize), at the optimal solution to the primal problem, all the
+we are trying to minimize), at the solution to the primal problem, all the
 training examples will have $\xi_i$ equal
 to zero.

CS545 fall 2016