I applied svm-train on a data set N of features and have
found out the cross validation accuracy
and then i have applied svm-train again on a data set
which is the subset of having K (K<N) features
and have found out the cross validation accuracy.
The accuracies in both cases are the same
and in fact for any subset with the root as original(N feature)
the accuracy is coming out to be the same,irrespective of number of features.
Should'nt the accuracy improve with reducing number of features??
Why the anomaly?