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: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org