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Posted to issues@spark.apache.org by "李力 (JIRA)" <ji...@apache.org> on 2015/06/10 07:01:00 UTC
[jira] [Updated] (SPARK-8284) Regualarized Extreme Learning Machine
for MLLib
[ https://issues.apache.org/jira/browse/SPARK-8284?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
李力 updated SPARK-8284:
----------------------
Description:
Extreme Learning Machine can get better generalization performance at a much faster learning speed for regression and classification problem，but the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task.
Through analyzing the mechanism of ELM algorithm , an efficient parallel ELM for regression is designed and implemented based on Spark.
The experimental results demonstrate that the propose parallel ELM for regression can efficiently handle very large dataset with a good performance.
was:
Extreme Learning Machine can get better generalization performance at a mauch faster learning speed for regression and classification problem，but the enlarging volume of datasets makes regression by ELM on very large scala datasets a challenging task.
Through analyzing the mechanism of ELM algorithm , an efficient parallel ELM for regression is designed and implemented based on Spark.
The experimental results demonstrate that the propose parallel ELM for regresssion can efficiently handle very large dataset with a good performance.
> Regualarized Extreme Learning Machine for MLLib
> -----------------------------------------------
>
> Key: SPARK-8284
> URL: https://issues.apache.org/jira/browse/SPARK-8284
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.3.1
> Reporter: 李力
>
> Extreme Learning Machine can get better generalization performance at a much faster learning speed for regression and classification problem，but the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task.
> Through analyzing the mechanism of ELM algorithm , an efficient parallel ELM for regression is designed and implemented based on Spark.
> The experimental results demonstrate that the propose parallel ELM for regression can efficiently handle very large dataset with a good performance.
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