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Posted to issues@spark.apache.org by "李力 (JIRA)" <ji...@apache.org> on 2015/06/08 16:54:00 UTC
[jira] [Issue Comment Deleted] (SPARK-2262) Extreme Learning
Machines (ELM) for MLLib
[ https://issues.apache.org/jira/browse/SPARK-2262?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
李力 updated SPARK-2262:
----------------------
Comment: was deleted
(was: The demo Spark codes of basic ELM (with randomly generated hidden nodes, random neurons) are available for Classification and Regression , These random hidden nodes include sigmoid .
The following sample of satimage is provided for you to try , Train files and testing files are text files, each raw consisting of information of one instance. First column are the expected output (target) for regression and classification applications, the rest columns consist of different attributes information of each instance.
author: lili
e-mail: 277609809@qq.com
company: Xi`an University of Posts & Telecommunications )
> Extreme Learning Machines (ELM) for MLLib
> -----------------------------------------
>
> Key: SPARK-2262
> URL: https://issues.apache.org/jira/browse/SPARK-2262
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Erik Erlandson
> Assignee: Erik Erlandson
> Labels: features
>
> MLLib has a gap in the NN space. There's some good reason for this, as batching gradient updates in traditional backprop training is known to not perform well.
> However, Extreme Learning Machines(ELM) combine support for nonlinear activation functions in a hidden layer with a batch-friendly linear training. There is also a body of ELM literature on various avenues for extension, including multi-category classification, multiple hidden layers and adaptive addition/deletion of hidden nodes.
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