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author | Martin Jaggi <m.jaggi@gmail.com> | 2014-02-08 11:39:13 -0800 |
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committer | Patrick Wendell <pwendell@gmail.com> | 2014-02-08 11:39:13 -0800 |
commit | fabf1749995103841e6a3975892572f376ee48d0 (patch) | |
tree | a9c03486cce6cc4f390405f33266a31861ebe3d4 /docs/mllib-optimization.md | |
parent | 3a9d82cc9e85accb5c1577cf4718aa44c8d5038c (diff) | |
download | spark-fabf1749995103841e6a3975892572f376ee48d0.tar.gz spark-fabf1749995103841e6a3975892572f376ee48d0.tar.bz2 spark-fabf1749995103841e6a3975892572f376ee48d0.zip |
Merge pull request #552 from martinjaggi/master. Closes #552.
tex formulas in the documentation
using mathjax.
and spliting the MLlib documentation by techniques
see jira
https://spark-project.atlassian.net/browse/MLLIB-19
and
https://github.com/shivaram/spark/compare/mathjax
Author: Martin Jaggi <m.jaggi@gmail.com>
== Merge branch commits ==
commit 0364bfabbfc347f917216057a20c39b631842481
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Fri Feb 7 03:19:38 2014 +0100
minor polishing, as suggested by @pwendell
commit dcd2142c164b2f602bf472bb152ad55bae82d31a
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 18:04:26 2014 +0100
enabling inline latex formulas with $.$
same mathjax configuration as used in math.stackexchange.com
sample usage in the linear algebra (SVD) documentation
commit bbafafd2b497a5acaa03a140bb9de1fbb7d67ffa
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 17:31:29 2014 +0100
split MLlib documentation by techniques
and linked from the main mllib-guide.md site
commit d1c5212b93c67436543c2d8ddbbf610fdf0a26eb
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 16:59:43 2014 +0100
enable mathjax formula in the .md documentation files
code by @shivaram
commit d73948db0d9bc36296054e79fec5b1a657b4eab4
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 16:57:23 2014 +0100
minor update on how to compile the documentation
Diffstat (limited to 'docs/mllib-optimization.md')
-rw-r--r-- | docs/mllib-optimization.md | 40 |
1 files changed, 40 insertions, 0 deletions
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md new file mode 100644 index 0000000000..428284ef29 --- /dev/null +++ b/docs/mllib-optimization.md @@ -0,0 +1,40 @@ +--- +layout: global +title: MLlib - Optimization +--- + +* Table of contents +{:toc} + + +# Gradient Descent Primitive + +[Gradient descent](http://en.wikipedia.org/wiki/Gradient_descent) (along with +stochastic variants thereof) are first-order optimization methods that are +well-suited for large-scale and distributed computation. Gradient descent +methods aim to find a local minimum of a function by iteratively taking steps +in the direction of the negative gradient of the function at the current point, +i.e., the current parameter value. Gradient descent is included as a low-level +primitive in MLlib, upon which various ML algorithms are developed, and has the +following parameters: + +* *gradient* is a class that computes the stochastic gradient of the function +being optimized, i.e., with respect to a single training example, at the +current parameter value. MLlib includes gradient classes for common loss +functions, e.g., hinge, logistic, least-squares. The gradient class takes as +input a training example, its label, and the current parameter value. +* *updater* is a class that updates weights in each iteration of gradient +descent. MLlib includes updaters for cases without regularization, as well as +L1 and L2 regularizers. +* *stepSize* is a scalar value denoting the initial step size for gradient +descent. All updaters in MLlib use a step size at the t-th step equal to +stepSize / sqrt(t). +* *numIterations* is the number of iterations to run. +* *regParam* is the regularization parameter when using L1 or L2 regularization. +* *miniBatchFraction* is the fraction of the data used to compute the gradient +at each iteration. + +Available algorithms for gradient descent: + +* [GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) + |