Projects running under agnostic.
Showing Items 1-20 of 225 on page 1 of 12: 1 2 3 4 5 6 Next Last

Logo pycobra regression analysis and ensemble toolkit 0.1.0

by bhargavvader - April 19, 2017, 15:04:14 CET [ Project Homepage BibTeX Download ] 136 views, 14 downloads, 2 subscriptions

About: pycobra is a python toolkit to help with regression analysis and visualisation. It provides an implementation of the COBRA predictor aggregation algorithm.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-CoxBoost 1.4

by r-cran-robot - April 1, 2017, 00:00:04 CET [ Project Homepage BibTeX Download ] 32785 views, 6234 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:04.728359


Logo r-cran-e1071 1.6-8

by r-cran-robot - April 1, 2017, 00:00:04 CET [ Project Homepage BibTeX Download ] 35995 views, 7214 downloads, 3 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 1 vote)

About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:04.887707


Logo r-cran-Boruta 5.2.0

by r-cran-robot - April 1, 2017, 00:00:03 CET [ Project Homepage BibTeX Download ] 24786 views, 5014 downloads, 2 subscriptions

About: Wrapper Algorithm for All Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:03.778215


Logo r-cran-CORElearn 1.50.3

by r-cran-robot - March 28, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 16695 views, 3523 downloads, 1 subscription

About: Classification, Regression and Feature Evaluation

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:04.175765


Logo r-cran-arules 1.5-2

by r-cran-robot - March 12, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 39212 views, 8256 downloads, 3 subscriptions

About: Mining Association Rules and Frequent Itemsets

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:02.304127


Logo scikit multilearn 0.0.5

by niedakh - February 25, 2017, 03:51:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2296 views, 499 downloads, 3 subscriptions

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.

Changes:
  • a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
  • support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
  • support for label powerset based stratified kfold added
  • graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
  • bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

Logo r-cran-biglasso 1.3-3

by r-cran-robot - January 24, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 604 views, 106 downloads, 2 subscriptions

About: Extending Lasso Model Fitting to Big Data

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:03.507268


Logo NaN toolbox 3.1.2

by schloegl - January 22, 2017, 12:24:59 CET [ Project Homepage BibTeX Download ] 57628 views, 11691 downloads, 3 subscriptions

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes:

Changes in v.3.1.2 - improve configuration and build system - support of more platforms (including Octave 4.2.0) improved

Changes in v.3.0.3 - improve compatibility for Octave on Windows

Changes in v.3.0.1 - fix packaging for octave

Changes in v.2.8.5 - bug fix: trimmean - compiler support for gcc-5 and clang - fix typos

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG


Logo python weka wrapper3 0.1.2

by fracpete - January 4, 2017, 10:27:40 CET [ Project Homepage BibTeX Download ] 2130 views, 398 downloads, 3 subscriptions

About: A thin Python3 wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • "typeconv.double_matrix_to_ndarray" no longer assumes a square matrix (https://github.com/fracpete/python-weka-wrapper3/issues/4)
  • "len(Instances)" now returns the number of rows in the dataset (module "weka.core.dataset")
  • added method "insert_attribute" to the "Instances" class
  • added class method "create_relational" to the "Attribute" class
  • upgraded Weka to 3.9.1

Logo python weka wrapper 0.3.10

by fracpete - January 4, 2017, 10:21:33 CET [ Project Homepage BibTeX Download ] 42688 views, 8464 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • "types.double_matrix_to_ndarray" no longer assumes a square matrix (https://github.com/fracpete/python-weka-wrapper/issues/48)
  • "len(Instances)" now returns the number of rows in the dataset (module "weka.core.dataset")
  • added method "insert_attribute" to the "Instances" class
  • added class method "create_relational" to the "Attribute" class
  • upgraded Weka to 3.9.1

Logo ADAMS 16.12.1

by fracpete - December 22, 2016, 05:24:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26501 views, 4926 downloads, 3 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

Changes:

Some highlights:

  • Over 80 new actors, nearly 30 new conversions
  • Weka Investigator -- the big brother of the Weka Explorer, or how to be more efficient with less clicks using multiple datasets in multiple sessions and multiple predefined outputs per evaluation run
  • Weka Multi-Experimenter -- simple interface for running Weka and ADAMS experiments.
  • File commander -- dual-pane file manager (inspired by Norton/Midnight commander) that allows you to manage local and remote files (ftp, sftp, smb); usually faster than native file managers (like Windows Explorer, Nautilus, Caja) in terms of handling 10s of thousand of files in a single directory
  • experimental deeplearning4j module
  • module for querying/consuming webservices using Groovy
  • basic terminal-based GUI for remote machines (eg cloud)
  • many interactive actors can be used in headless environment now as well
  • Fixed a memory leak introduced by Java's logging framework
  • Flow editor now has predefined rules for swapping actors, e.g. Trigger with Tee or ConditionalTrigger, maintaining as many options as possible (including any sub-actors).
  • improved imaging and PDF support

Logo r-cran-caret 6.0-73

by r-cran-robot - November 8, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 118592 views, 22644 downloads, 3 subscriptions

About: Classification and Regression Training

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:04.074456


Logo Tools for Regression and Classification 1.0.0

by matloff - October 29, 2016, 08:22:40 CET [ Project Homepage BibTeX Download ] 1581 views, 255 downloads, 3 subscriptions

About: Toolkit for parametric and nonparametric regression and classification.

Changes:

Initial Announcement on mloss.org.


Logo rectools a Novel Toolbox for Recommender Systems 1.0.0

by matloff - October 29, 2016, 07:41:58 CET [ Project Homepage BibTeX Download ] 1422 views, 265 downloads, 2 subscriptions

Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty Star
(based on 1 vote)

About: Novel R toolbox for collaborative filtering recommender systems.

Changes:

Initial Announcement on mloss.org.


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 4.0

by hn - October 19, 2016, 10:15:05 CET [ Project Homepage BibTeX Download ] 42443 views, 9416 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 2 votes)

About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models.

Changes:

A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include grid-based covariance approximations natively.

More generic sparse approximation using Power EP

  • unified treatment of FITC approximation, variational approaches VFE and hybrids

  • inducing input optimisation for all (compositions of) covariance functions dropping the previous limitation to a few standard examples

  • infFITC is now covered by the more generic infGaussLik function

Approximate covariance object unifying sparse approximations, grid-based approximations and exact covariance computations

  • implementation in cov/apx, cov/apxGrid, cov/apxSparse

  • generic infGaussLik unifies infExact, infFITC and infGrid

  • generic infLaplace unifies infLaplace, infFITC_Laplace and infGrid_Laplace

Hiearchical structure of covariance functions

  • clear hierachical compositional implementation

  • no more code duplication as present in covSEiso and covSEard pairs

  • two mother covariance functions

    • covDot for dot-product-based covariances and

    • covMaha for Mahalanobis-distance-based covariances

  • a variety of modifiers: eye, iso, ard, proj, fact, vlen

  • more flexibility as more variants are available and possible

  • all covariance functions offer derivatives w.r.t. inputs

Faster derivative computations for mean and cov functions

  • switched from partial derivatives to directional derivatives

  • simpler and more concise interface of mean and cov functions

  • much faster marginal likelihood derivative computations

  • simpler and more compact code

New mean functions

  • new mean/meanWSPC (Weighted Sum of Projected Cosines or Random Kitchen Sink features) following a suggestion by William Herlands

  • new mean/meanWarp for constructing a new mean from an existing one by means of a warping function adapted from William Herlands

New optimizer

  • added a new minimize_minfunc, contributed by Truong X. Nghiem

New GLM link function

  • added the twice logistic link function util/glm_invlink_logistic2

Smaller fixes

  • two-fold speedup of util/elsympol used by covADD by Truong X. Nghiem

  • bugfix in util/logphi as reported by John Darby


Logo r-cran-bst 0.3-14

by r-cran-robot - September 12, 2016, 00:00:00 CET [ Project Homepage BibTeX Download ] 7503 views, 1745 downloads, 2 subscriptions

About: Gradient Boosting

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:03.875727


Logo Sparse Compositional Metric Learning v1.11

by bellet - August 2, 2016, 11:43:03 CET [ BibTeX BibTeX for corresponding Paper Download ] 6021 views, 1799 downloads, 3 subscriptions

About: Scalable learning of global, multi-task and local metrics from data

Changes:

Minor bug fix in multi-task objective computation (thanks to Junjie Hu).


Logo JMLR GPstuff 4.7

by avehtari - June 9, 2016, 17:45:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41985 views, 10296 downloads, 3 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

Changes:

2016-06-09 Version 4.7

Development and release branches available at https://github.com/gpstuff-dev/gpstuff

New features

  • Simple Bayesian Optimization demo

Improvements

  • Improved use of PSIS
  • More options added to gp_monotonic
  • Monotonicity now works for additive covariance functions with selected variables
  • Possibility to use gpcf_squared.m-covariance function with derivative observations/monotonicity
  • Default behaviour made more robust by changing default jitter from 1e-9 to 1e-6
  • LA-LOO uses the cavity method as the default (see Vehtari et al (2016). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. JMLR, accpeted for publication)
  • Selected variables -option works now better with monotonicity

Bugfixes

  • small error in derivative observation computation fixed
  • several minor bug fixes

Logo AutoWEKA 2.0

by larsko - May 19, 2016, 19:58:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2273 views, 721 downloads, 3 subscriptions

About: Automatically finds the best model with its best parameter settings for a given classification or regression task.

Changes:

Initial Announcement on mloss.org.


Showing Items 1-20 of 225 on page 1 of 12: 1 2 3 4 5 6 Next Last