Saturday 13 December 2014

Anomaly Detection v.s. Supervised Learning

Anomaly Detection

Fraud detection, manufacturing, Monitoring machines in data center
  • Very small number of positive examples(y=1).(0-20 is common)
  • Large number of negative(y=0) examples.
  • Many different "types" of anomalies. Hard for any algorithm to learn from positive examples what the anomalies look like.
  • Future anomalies may look nothing like any of the anomalous examples we've seen so far.

Supervised Learning
Email spam, weather prediction, cancer classification.
  • Large number of positive and negative examples.
  • Enough positive examples for algorithm to get a sense of what positive example are like, future positive examples likely to be similar to ones in training set.



No comments:

Post a Comment