title: 10 fold cross validation
body: <p>In k fold we have this:
  you divide the data into k subsets of
  (approximately) equal size. You train the net k times, each time leaving
  out one of the subsets from training, but using only the omitted subset to
  compute whatever error criterion interests you. If k equals the sample
  size, this is called "leave-one-out" cross-validation. "Leave-v-out" is a
  more elaborate and expensive version of cross-validation that involves
  leaving out all possible subsets of v cases.</p>
  
  <p>what the Term training and testing mean?I can't understand.</p>
  
  <p>would you please tell me some references where I can learn this algorithm with an example?</p>
  
  <pre><code>Train classifier on folds: 2 3 4 5 6 7 8 9 10; Test against fold: 1
  Train classifier on folds: 1 3 4 5 6 7 8 9 10; Test against fold: 2
  Train classifier on folds: 1 2 4 5 6 7 8 9 10; Test against fold: 3
  Train classifier on folds: 1 2 3 5 6 7 8 9 10; Test against fold: 4
  Train classifier on folds: 1 2 3 4 6 7 8 9 10; Test against fold: 5
  Train classifier on folds: 1 2 3 4 5 7 8 9 10; Test against fold: 6
  Train classifier on folds: 1 2 3 4 5 6 8 9 10; Test against fold: 7
  Train classifier on folds: 1 2 3 4 5 6 7 9 10; Test against fold: 8
  Train classifier on folds: 1 2 3 4 5 6 7 8 10; Test against fold: 9
  Train classifier on folds: 1 2 3 4 5 6 7 8 9;  Test against fold: 10  
  </code></pre>