title: How many samples are optimal in one class using k-nearest neighbor?
body: <p>I have implemented k-nearest algorithm in my system. It consists from 26 classes, each of 100 samples. In my case, K=7 and it was completely trial and error to get the best classification result.</p>
  
  <p>I know that K should be chosen wisely to reduce the noise on the classification. But what about the number of samples? Is there any general rule such as "the more samples the better result"? Does it depend on something?</p>
  
  <p>Thank you for all your responses.</p>