spark-reviews mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: L-BFGS Documentation
Date Fri, 09 May 2014 00:35:21 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/702#discussion_r12460419
  
    --- Diff: docs/mllib-optimization.md ---
    @@ -163,3 +177,100 @@ each iteration, to compute the gradient direction.
     Available algorithms for gradient descent:
     
     * [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
    +
    +### Limited-memory BFGS
    +L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to
use L-BFGS in various 
    +ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the
gradient of objective
    +function, and updater into optimizer yourself instead of using the training APIs like

    +[LogisticRegression.LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegression).
    +See the example below. It will be addressed in the next release. 
    +
    +The L1 regularization by using 
    +[Updater.L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.Updater)
will not work since the 
    --- End diff --
    
    `L1Updater` is not under `Updater`. Should change to
    
    ~~~
    [L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater)
    ~~~


---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.
---

Mime
View raw message