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Posted to commits@hivemall.apache.org by my...@apache.org on 2016/12/01 05:26:01 UTC
[37/50] [abbrv] incubator-hivemall git commit: Update README.md
Update README.md
Project: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/commit/190cb6e7
Tree: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/tree/190cb6e7
Diff: http://git-wip-us.apache.org/repos/asf/incubator-hivemall/diff/190cb6e7
Branch: refs/heads/master
Commit: 190cb6e79859bd3941883e9ea290a8db59d9f968
Parents: 2890954
Author: Makoto YUI <yu...@gmail.com>
Authored: Sat Nov 12 15:40:22 2016 +0900
Committer: GitHub <no...@github.com>
Committed: Sat Nov 12 15:40:22 2016 +0900
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README.md | 153 +++------------------------------------------------------
1 file changed, 6 insertions(+), 147 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-hivemall/blob/190cb6e7/README.md
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diff --git a/README.md b/README.md
index 79333aa..1aa9fea 100644
--- a/README.md
+++ b/README.md
@@ -25,163 +25,22 @@ Apache Hivemall: Hive scalable machine learning library
[](https://github.com/myui/hivemall/blob/master/LICENSE)
[](https://coveralls.io/github/myui/hivemall)
-**News:** Hivemall joins [Apache Incubator](http://hivemall.incubator.apache.org/)! :tada: Currently in the process of moving the project repository to ASF.
+**News:** Hivemall joins [Apache Incubator](http://hivemall.incubator.apache.org/)! :tada: Currently in the process of moving the project repository to ASF. This repository will be deprecated soon. Please move your stars and forks to [the new repository](https://github.com/apache/incubator-hivemall).
+
[](http://hivemall.incubator.apache.org/)
-Hivemall is a scalable machine learning library that runs on Apache Hive.
-Hivemall is designed to be scalable to the number of training instances as well as the number of training features.
+Hivemall is a scalable machine learning library that runs on Apache Hive, Apache Spark, and Apache Pig. Hivemall is designed to be scalable to the number of training instances as well as the number of training features.
-Supported Algorithms
---------------------
-
-Hivemall provides machine learning functionality as well as feature engineering functions through UDFs/UDAFs/UDTFs of Hive.
-
-## Binary Classification
-
-* [Perceptron](http://en.wikipedia.org/wiki/Perceptron)
-
-* Passive Aggressive (PA, PA1, PA2)
-
-* Confidence Weighted (CW)
-
-* Adaptive Regularization of Weight Vectors (AROW)
-
-* Soft Confidence Weighted (SCW1, SCW2)
-
-* AdaGradRDA (w/ hinge loss)
-
-* Factorization Machine (w/ logistic loss)
-
-_My recommendation is AROW, SCW1, AdaGradRDA, and Factorization Machine while it depends._
-
-## Multi-class Classification
-
-* [Perceptron](http://en.wikipedia.org/wiki/Perceptron)
-
-* Passive Aggressive (PA, PA1, PA2)
-
-* Confidence Weighted (CW)
-
-* Adaptive Regularization of Weight Vectors (AROW)
-
-* Soft Confidence Weighted (SCW1, SCW2)
-
-* Random Forest Classifier
-
-* Gradient Tree Boosting (_Experimental_)
-
-_My recommendation is AROW and SCW while it depends._
-
-## Regression
-
-* [Logistic Regression](http://en.wikipedia.org/wiki/Logistic_regression) using [Stochastic Gradient Descent](http://en.wikipedia.org/wiki/Stochastic_gradient_descent)
-
-* AdaGrad, AdaDelta (w/ logistic Loss)
-
-* Passive Aggressive Regression (PA1, PA2)
-
-* AROW regression
-
-* Random Forest Regressor
-
-* Factorization Machine (w/ squared loss)
-
-* [Polynomial Regression](http://en.wikipedia.org/wiki/Polynomial_regression)
-
-_My recommendation for is AROW regression, AdaDelta, and Factorization Machine while it depends._
-
-## Recommendation
-
-* [Minhash](http://en.wikipedia.org/wiki/MinHash) ([LSH](http://en.wikipedia.org/wiki/Locality-sensitive_hashing) with jaccard index)
-
-* [Matrix Factorization](http://en.wikipedia.org/wiki/Matrix_decomposition) (sgd, adagrad)
-
-* Factorization Machine (squared loss for rating prediction)
-
-## k-Nearest Neighbor
-
-* [Minhash](http://en.wikipedia.org/wiki/MinHash) ([LSH](http://en.wikipedia.org/wiki/Locality-sensitive_hashing) with jaccard index)
-
-* b-Bit minhash
-
-* Brute-force search using Cosine similarity
-
-## Anomaly Detection
-
-* [Local Outlier Factor (LOF)](http://en.wikipedia.org/wiki/Local_outlier_factor)
-
-## Natural Language Processing
-
-* English/Japanese Text Tokenizer
-
-## Feature engineering
-
-* [Feature Hashing](http://en.wikipedia.org/wiki/Feature_hashing) (MurmurHash, SHA1)
-
-* [Feature scaling](http://en.wikipedia.org/wiki/Feature_scaling) (Min-Max Normalization, Z-Score)
-
-* [Polynomial Features](http://en.wikipedia.org/wiki/Polynomial_kernel)
-
-* Feature instances amplifier that reduces iterations on training
-
-* [TF-IDF](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) vectorizer
-
-* Bias clause
-
-* Data generator for one-vs-the-rest classifiers
-
-System requirements
---------------------
-
-* Hive 0.13 or later
-
-* Java 7 or later
-
-* Spark 1.6 or 2.0 for Hivemall on Spark
-
-* Pig 0.15 or later for Hivemall on Pig
-
Basic Usage
------------
-[](http://www.slideshare.net/myui/hivemall-hadoop-summit-2014-san-jose)
+[](http://www.slideshare.net/myui/hadoopsummit16-myui)
-Find more examples on [our wiki page](https://github.com/myui/hivemall/wiki/) and find a brief introduction to Hivemall in [this slide](http://www.slideshare.net/myui/hivemall-hadoop-summit-2014-san-jose).
-
-Copyright
----------
-
-```
-Copyright (C) 2015-2016 Makoto YUI
-Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
-```
-
-Put the above copyrights for the services/softwares that use Hivemall.
+Find more examples on [our user guide](http://hivemall.incubator.apache.org/userguide/index.html) and find a brief introduction to Hivemall in [this slide](http://www.slideshare.net/myui/hadoopsummit16-myui).
Support
-------
-Support is through the [issue list](https://github.com/myui/hivemall/issues?q=label%3Aquestion), not by a direct e-mail.
-
-References
-----------
-
-Please refer the following paper for research uses:
-
-* Makoto Yui. ``Hivemall: Scalable Machine Learning Library for Apache Hive'', [2014 Hadoop Summit](http://hadoopsummit.org/san-jose/), June 2014. \[[slide](http://www.slideshare.net/myui/hivemall-hadoop-summit-2014-san-jose)]
-
-* Makoto Yui and Isao Kojima. ``Hivemall: Hive scalable machine learning library'' (demo), [NIPS 2013 Workshop on Machine Learning Open Source Software: Towards Open Workflows](https://mloss.org/workshop/nips13/), Dec 2013.
-
-* Makoto Yui and Isao Kojima. ``A Database-Hadoop Hybrid Approach to Scalable Machine Learning'', Proc. IEEE 2nd International Congress on Big Data, July 2013 \[[paper](http://staff.aist.go.jp/m.yui/publications/bigdata2013myui.pdf)\] \[[slide](http://www.slideshare.net/myui/bigdata2013myui)\]
-
-Awards
-------
-
-* [InfoWorld Bossie Awards 2014: The best open source big data tools](http://www.infoworld.com/article/2688074/big-data/big-data-164727-bossie-awards-2014-the-best-open-source-big-data-tools.html)
-
-Acknowledgment
---------------
-
-This work was supported in part by a JSPS grant-in-aid for young scientists (B) #24700111 and a JSPS grant-in-aid for scientific research (A) #24240015.
+Support is through the [user@hivemall.incubator.apache.org](http://hivemall.incubator.apache.org/mail-lists.html), not by a direct e-mail.