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Logo JMLR LPmade 1.2.2

by rlichten - April 2, 2012, 17:11:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 48126 views, 15001 downloads, 0 subscriptions

About: Link Prediction Made Easy

Changes:

v1.2.2

  • Fixed MAJOR issue related to github migration several months ago. The original github commit neglected to import empty folders. This caused parts of the project compilation procedure to fail. Any users of LPmade who downloaded the most recent version from github over the last several months would have encountered this build error and should download the most recent version. This change updates the network library makefile to create the empty folders and gets around the issue. Very sorry to anybody that this may have inconvenienced, but thanks for hanging in there if you diagnosed and solved it yourself.

  • Fixed issue with auroc on 32-bit architectures that caused integer wraparounds that produced incorrect results.


Logo r-cran-mboost 2.2-2

by r-cran-robot - February 8, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 77428 views, 14688 downloads, 0 subscriptions

About: Model-Based Boosting

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.324985


Logo r-cran-party 1.0-6

by r-cran-robot - January 9, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 62541 views, 14433 downloads, 0 subscriptions

About: A Laboratory for Recursive Partytioning

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.775432


Logo JMLR JKernelMachines 3.0

by dpicard - May 4, 2016, 17:53:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 72244 views, 14237 downloads, 0 subscriptions

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About: machine learning library in java for easy development of new kernels and kernel algorithms

Changes:

Version 3.0

/! Warning: this version is incompatible with previous code

  • change license to BSD 3-clauses
  • change package name to net.jkernelmachines
  • change to maven build system (available through central)
  • online training interfaces to allow continuous online learning
  • add a new budget oriented kernel classifier
  • new kernel and processing especially for strings

Logo Milk 0.5

by luispedro - November 7, 2012, 13:08:28 CET [ Project Homepage BibTeX Download ] 59159 views, 14132 downloads, 0 subscriptions

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About: Python Machine Learning Toolkit

Changes:

Added LASSO (using coordinate descent optimization). Made SVM classification (learning and applying) much faster: 2.5x speedup on yeast UCI dataset.


Logo APCluster 1.4.4

by UBod - July 28, 2017, 09:47:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 79202 views, 14039 downloads, 0 subscriptions

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About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes:
  • changed dependency to suggested package 'kebabs' to version of at least 1.5.4 for improved interoperability
  • bug fix in as.dendrogram() method with signature 'AggExResult'
  • added discrepancy metric to distance computations and updated src/distanceL.c to new version
  • registered C/C++ subroutines
  • minor change in the vignette template
  • moved NEWS to inst/NEWS
  • added inst/COPYRIGHT

Logo r-cran-e1071 1.6-8

by r-cran-robot - January 1, 2018, 00:00:07 CET [ Project Homepage BibTeX Download ] 65885 views, 13964 downloads, 0 subscriptions

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

Changes:

Fetched by r-cran-robot on 2018-01-01 00:00:07.696284


Logo JMLR Continuous Time Bayesian Network Reasoning and Learning Engine 1.1.1

by cshelton - December 9, 2013, 18:44:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 42115 views, 13014 downloads, 0 subscriptions

About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Changes:

compilation problems fixed


Logo Theano 1.0.2

by jaberg - May 23, 2018, 16:34:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 73633 views, 12972 downloads, 0 subscriptions

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 1.0.2 (23rd of May, 2018)

This is a maintenance release of Theano, version 1.0.2, with no new features, but some important bug fixes.

We recommend that everybody update to this version.

Highlights (since 1.0.1):

  • Theano should work under PyPy now (this is experimental).
  • Update for cuDNN 7.1 RNN API changes.
  • Fix for a crash related to mixed dtypes with cuDNN convolutions.
  • MAGMA should work in more cases without manual config.
  • Handle reductions with non-default accumulator dtype better on the GPU.
  • Improvements to the test suite so that it fails less often due to random chance.

A total of 6 people contributed to this release since 1.0.1:

  • Frederic Bastien
  • Steven Bocco
  • Jon Haygood
  • Arnaud Bergeron
  • Jordan Melendez
  • Desiree Vogt-Lee
  • Garming Sam
  • Pascal Lamblin
  • Vincent Dumoulin
  • Glexin
  • Simon Lefrancois

Logo r-cran-Boruta 6.0.0

by r-cran-robot - September 1, 2018, 00:00:04 CET [ Project Homepage BibTeX Download ] 56843 views, 12845 downloads, 0 subscriptions

About: Wrapper Algorithm for All Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2018-09-01 00:00:04.516878


Logo mldata-utils 0.5.0

by sonne - April 8, 2011, 10:02:44 CET [ Project Homepage BibTeX Download ] 59236 views, 12600 downloads, 0 subscriptions

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

Changes:
  • Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
  • Various bugfixes.

Logo SVDFeature, A Toolkit for Informative Collaborative Filtering 1.2.2

by crowwork - January 9, 2013, 02:21:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 81749 views, 12478 downloads, 0 subscriptions

About: SVDFeature is a toolkit for developing generic collaborative filtering algorithms by defining features.

Changes:

JMLR MLOSS version.


Logo JMLR MSVMpack 1.5.1

by lauerfab - March 9, 2017, 12:29:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 48250 views, 12340 downloads, 0 subscriptions

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Fix compilation error with recent gcc

Logo JMLR Mulan 1.5.0

by lefman - February 23, 2015, 21:19:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 44505 views, 12117 downloads, 0 subscriptions

About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.

Changes:

Learners

  • MLCSSP.java: Added the MLCSSP algorithm (from ICML 2013)
  • Enhancements of multi-target regression capabilities
  • Improved CLUS support
  • Added pairwise classifier and pairwise transformation

Measures/Evaluation

  • Providing training data in the Evaluator is unnecessary in the case of specific measures.
  • Examples with missing ground truth are not skipped for measures that handle missing values.
  • Added logistics and squared error losses and measures

Bug fixes

  • IndexOutOfBounds in calculation of MiAP and GMiAP
  • Bug fix in Rcut.java
  • When in rank/score mode the meta-data contained additional unecessary attributes. (Newton Spolaor)

API changes

  • Upgrade to Java 7
  • Upgrade to Weka 3.7.10

Miscalleneous

  • Small changes and improvements in the wrapper classes for the CLUS library
  • ENTCS13FeatureSelection.java (new experiment)
  • Enumeration is now used for specifying the type of meta-data. (Newton Spolaor)

Logo Somoclu 1.7.5

by peterwittek - March 1, 2018, 23:30:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 66215 views, 12009 downloads, 0 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

Changes:
  • New: A Makefile for mingw to build on Windows.
  • Changed: PR #94 added a much more efficient sparse kernel.
  • Changed: boilerplate code for Julia greatly improved.
  • Changed: Code cleanup, pre-processor macros simplified.
  • Changed: Adapted to Seaborn API changes in plotting heatmaps.

Logo JMLR The Generalised Linear Models Inference and Estimation Toolbox 1.5

by hn - November 8, 2013, 13:58:03 CET [ Project Homepage BibTeX Download ] 47474 views, 11600 downloads, 0 subscriptions

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

Changes:

added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes

generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used


Logo r-cran-RWeka 0.4-10

by r-cran-robot - January 10, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 49249 views, 11383 downloads, 0 subscriptions

About: R/Weka interface

Changes:

Fetched by r-cran-robot on 2012-02-01 00:00:11.330277


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 71166 views, 11335 downloads, 0 subscriptions

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 51370 views, 11253 downloads, 0 subscriptions

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

Changes:

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)


Logo LIBOL 0.3.0

by stevenhoi - December 12, 2013, 15:26:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32325 views, 11179 downloads, 0 subscriptions

About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification.

Changes:

In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows:

• Add a template and guide for adding new algorithms;

• Improve parameter settings and make documentation clear;

• Improve documentation on data formats and key functions;

• Amend the "OGD" function to use different loss types;

• Fixed some name inconsistency and other minor bugs.


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