All entries.
Showing Items 21-30 of 573 on page 3 of 58: Previous 1 2 3 4 5 6 7 8 Next Last

Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5223 views, 836 downloads, 3 subscriptions

About: Hivemall is a scalable machine learning library running on Hive/Hadoop.

Changes:
  • Supported Matrix Factorization
  • Added a support for TF-IDF computation
  • Supported AdaGrad/AdaDelta
  • Supported AdaGradRDA classification
  • Added normalization scheme

Logo XGBoost v0.3.95

by crowwork - March 9, 2015, 23:17:29 CET [ Project Homepage BibTeX Download ] 5520 views, 1100 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

Changes:

New features in the lastest changes

  • Distributed version now runs on Hadoop YARN

Logo libcmaes 0.9.5

by beniz - March 9, 2015, 09:05:22 CET [ Project Homepage BibTeX Download ] 4548 views, 961 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

Changes:

This is a major release, with several novelties, improvements and fixes, among which:

  • step-size two-point adaptaion scheme for improved performances in some settings, ref #88

  • important bug fixes to the ACM surrogate scheme, ref #57, #106

  • simple high-level workflow under Python, ref #116

  • improved performances in high dimensions, ref #97

  • improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

  • elitist mechanism for forcing best solutions during evolution, ref 103

  • new legacy plotting function, ref #110

  • optional initial function value, ref #100

  • improved C++ API, ref #89

  • Python bindings support with Anaconda, ref #111

  • configure script now tries to detect numpy when building Python bindings, ref #113

  • Python bindings now have embedded documentation, ref #114

  • support for Travis continuous integration, ref #122

  • lower resolution random seed initialization


Logo r-cran-C50 0.1.0-24

by r-cran-robot - March 8, 2015, 00:00:00 CET [ Project Homepage BibTeX Download ] 3903 views, 931 downloads, 0 subscriptions

About: C5.0 Decision Trees and Rule-Based Models

Changes:

Fetched by r-cran-robot on 2015-04-01 00:00:04.284719


Logo ADAMS 0.4.8

by fracpete - March 4, 2015, 00:54:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10377 views, 2309 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:
  • 13 new actors
  • 1 new conversion
  • new module adams-access: for accessing MS Access databases (read/write)
  • adams-heatmap module overhaul
  • adams-imaging: barcode (QRCode etc) encoding/decoding, multi-image operations (and, or, xor)
  • Flow editor gets a "quick edit" tab
  • MEKA upgraded to 1.7.5
  • Weka filter "Scale" (unsupervised/instance) allows you to scale the values of a row eg to interval 0 to 1
  • SimplePlot sink is a "dumbed down" version of the SequencePlotter with only basic options -- enough to create good looking plots quickly
  • Upper/LowerCase conversion take the locale into account now
  • added print support for PDFs
  • fixed sluggish behavior in Flow editor (open/save/undo)
  • TryCatch correctly flushes token now
  • spreadsheet column range/index sometimes failed in conjunction with variables
  • fixed memory leak in Weka Explorer plugins FixedClassifierErrorPlot, ThresholdCurve
  • WekaExcel upgraded to 1.0.5 (no longer omits last row in sheets)
  • WhileLoop did not react to changes in variables once looping, ie conditions couldn't make use of variables
  • ImageProcessor now works again with the improved ImageFileChooser dialog
  • PreviewBrowser displays arrays in a more meaningful way
  • WekaFileReader didn't output empty datasets in DATASET mode
  • obtaining subsets from Notes objects only resulted in first element being retrieved

Logo JMLR dlib ml 18.14

by davis685 - March 1, 2015, 23:51:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 96760 views, 16768 downloads, 3 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds an implementation of spectral clustering as well as a few bug fixes and usability improvements.


Logo JMLR Mulan 1.5.0

by lefman - February 23, 2015, 21:19:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16708 views, 6577 downloads, 2 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 CN24 Convolutional Neural Networks for Semantic Segmentation 1.0

by erik - February 23, 2015, 09:02:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 702 views, 116 downloads, 1 subscription

About: CN24 is a complete semantic segmentation framework using fully convolutional networks.

Changes:

Initial Announcement on mloss.org.


Logo Machine Learning Support System MALSS 0.5.0

by canard0328 - February 20, 2015, 15:56:02 CET [ Project Homepage BibTeX Download ] 503 views, 114 downloads, 1 subscription

About: MALSS is a python module to facilitate machine learning tasks.

Changes:

Initial Announcement on mloss.org.


Logo JMLR DLLearner 1.0

by Jens - February 13, 2015, 11:39:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15938 views, 4008 downloads, 6 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 3 votes)

About: The DL-Learner framework contains several algorithms for supervised concept learning in Description Logics (DLs) and OWL.

Changes:

See http://dl-learner.org/development/changelog/.


Showing Items 21-30 of 573 on page 3 of 58: Previous 1 2 3 4 5 6 7 8 Next Last