Return-Path: X-Original-To: apmail-spark-commits-archive@minotaur.apache.org Delivered-To: apmail-spark-commits-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 796F7179B4 for ; Mon, 20 Apr 2015 20:47:07 +0000 (UTC) Received: (qmail 55496 invoked by uid 500); 20 Apr 2015 20:47:07 -0000 Delivered-To: apmail-spark-commits-archive@spark.apache.org Received: (qmail 55468 invoked by uid 500); 20 Apr 2015 20:47:07 -0000 Mailing-List: contact commits-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list commits@spark.apache.org Received: (qmail 55459 invoked by uid 99); 20 Apr 2015 20:47:07 -0000 Received: from git1-us-west.apache.org (HELO git1-us-west.apache.org) (140.211.11.23) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 20 Apr 2015 20:47:07 +0000 Received: by git1-us-west.apache.org (ASF Mail Server at git1-us-west.apache.org, from userid 33) id 1A98EE098A; Mon, 20 Apr 2015 20:47:07 +0000 (UTC) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit From: meng@apache.org To: commits@spark.apache.org Message-Id: X-Mailer: ASF-Git Admin Mailer Subject: spark git commit: [doc][streaming] Fixed broken link in mllib section Date: Mon, 20 Apr 2015 20:47:07 +0000 (UTC) Repository: spark Updated Branches: refs/heads/master 97fda73db -> 517bdf36a [doc][streaming] Fixed broken link in mllib section The commit message is pretty self-explanatory. Author: BenFradet Closes #5600 from BenFradet/master and squashes the following commits: 108492d [BenFradet] [doc][streaming] Fixed broken link in mllib section Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/517bdf36 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/517bdf36 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/517bdf36 Branch: refs/heads/master Commit: 517bdf36aecdc94ef569b68f0a96892e707b5c7b Parents: 97fda73 Author: BenFradet Authored: Mon Apr 20 13:46:55 2015 -0700 Committer: Xiangrui Meng Committed: Mon Apr 20 13:46:55 2015 -0700 ---------------------------------------------------------------------- docs/streaming-programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/517bdf36/docs/streaming-programming-guide.md ---------------------------------------------------------------------- diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 262512a..2f2fea5 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -1588,7 +1588,7 @@ See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more abo *** ## MLlib Operations -You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. (Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details. +You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details. *** --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscribe@spark.apache.org For additional commands, e-mail: commits-help@spark.apache.org