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Posted to issues@spark.apache.org by "Stephanie Bodoff (JIRA)" <ji...@apache.org> on 2016/02/23 17:46:18 UTC
[jira] [Closed] (SPARK-13443) MLlib documentation displays Math
Processing Error
[ https://issues.apache.org/jira/browse/SPARK-13443?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Stephanie Bodoff closed SPARK-13443.
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Resolution: Cannot Reproduce
It was Chrome, but I'm no longer seeing the problem. Probably a transient issue.
> MLlib documentation displays Math Processing Error
> --------------------------------------------------
>
> Key: SPARK-13443
> URL: https://issues.apache.org/jira/browse/SPARK-13443
> Project: Spark
> Issue Type: Bug
> Components: Documentation
> Reporter: Stephanie Bodoff
> Attachments: Screen Shot 2016-02-22 at 5.09.30 PM.png
>
>
> http://spark.apache.org/docs/latest/mllib-linear-methods.html
> Many standard machine learning methods can be formulated as a convex optimization problem, i.e. the task of finding a minimizer of a convex function [Math Processing Error] that depends on a variable vector [Math Processing Error] (called weights in the code), which has [Math Processing Error] entries. Formally, we can write this as the optimization problem [Math Processing Error], where the objective function is of the form [Math Processing Error] Here the vectors [Math Processing Error] are the training data examples, for [Math Processing Error], and [Math Processing Error] are their corresponding labels, which we want to predict. We call the method linear if [Math Processing Error] can be expressed as a function of [Math Processing Error] and [Math Processing Error]. Several of spark.mllib’s classification and regression algorithms fall into this category, and are discussed here.
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