Rakesh Chalasani created SPARK-6867:
---------------------------------------
Summary: Dropout regularization
Key: SPARK-6867
URL: https://issues.apache.org/jira/browse/SPARK-6867
Project: Spark
Issue Type: New Feature
Components: MLlib
Reporter: Rakesh Chalasani
Priority: Minor
Linear models is MLLIB so far support no regularization, L1 and L2. Another
more recently popularized method for regularization is dropout
[http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf]. The dropout
regularization basically randomly omit some of the input features at each
iteration.
Though this approach is particularly used in training deep networks, they could
also be very useful on a linear models as if promotes adaptive regularization.
This approach is particularly useful in NLP
[http://papers.nips.cc/paper/4882-dropout-training-as-adaptive-regularization.pdf]
and, because of its simplicity can be easily adopted for streaming linear
models as well.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
