Brain Dump

Supervised Classifier

Tags
text-processing adaptive-intelligence

Is a classifier that is trained through supervised learning. Eg. bayes-classifier

We generally supply the classifier with [see page 10, 3 sets] of data:

  1. Training -> estimate probability distribution of discrete classifications.
  2. Development -> Used to fine tune classifier parameters to select best model.
  3. Test -> Evalaute the classifier (make sure it doesn't reproduce training result)

Training data should be:

AspectMeaning
[see page 28, representative]Even if the result is wrong, it should be derivable from the training data.
[see page 33, large]To avoid missing words in our vocabulary leading to inaccurate classifications.