Projects that are tagged with stochastic gradient descent.


Logo revrand 1.0.0

by dsteinberg - January 29, 2017, 04:33:54 CET [ Project Homepage BibTeX Download ] 11915 views, 2442 downloads, 3 subscriptions

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About: A library of scalable Bayesian generalised linear models with fancy features

Changes:
  • 1.0 release!
  • Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
  • Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
  • Random state can be set for all random objects
  • Numerous small improvements to make revrand production ready
  • Final report
  • Documentation improvements

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 2131 views, 452 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Changes:

Initial Announcement on mloss.org.


Logo Semi Stochastic Gradient Descent 1.0

by konkey - July 9, 2014, 04:28:47 CET [ BibTeX BibTeX for corresponding Paper Download ] 4003 views, 988 downloads, 1 subscription

About: Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized).

Changes:

Initial Announcement on mloss.org.


Logo SGD 2.0

by leonbottou - October 11, 2011, 20:59:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15537 views, 2423 downloads, 5 subscriptions

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About: The SGD-2.0 package contains implementations of the SGD and ASGD algorithms for linear SVMs and linear CRFs.

Changes:

Version 2.0 features ASGD.


Logo sofia ml 0.1

by dsculley - December 29, 2009, 23:30:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7391 views, 1313 downloads, 0 comments, 1 subscription

About: A fast implementation of several stochastic gradient descent learners for classification, ranking, and ROC area optimization, suitable for large, sparse data sets. Includes Pegasos SVM, SGD-SVM, Passive-Aggressive Perceptron, Perceptron with Margins, Logistic Regression, and ROMMA. Commandline utility and API libraries are provided.

Changes:

Initial Announcement on mloss.org.


Logo LibSGDQN 1.1

by antojne - July 2, 2009, 15:02:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10197 views, 2033 downloads, 1 subscription

About: LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs.

Changes:

small bug fix (thx nicolas ;)


Logo CRFsuite 0.8

by chokkan - March 18, 2009, 15:19:02 CET [ Project Homepage BibTeX Download ] 7571 views, 1863 downloads, 1 subscription

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About: CRFSuite is a speed-oriented implementation of Conditional Random Fields (CRFs). This software features: parameter estimation using SGD and L-BFGS, l1/l2 regularization, simple data I/O format, etc.

Changes:

Initial Announcement on mloss.org.