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Showing Items 31-40 of 582 on page 4 of 59: Previous 1 2 3 4 5 6 7 8 9 Next Last

Logo Loom 0.2.10

by fritzo - March 19, 2015, 19:22:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 661 views, 129 downloads, 2 subscriptions

About: A streaming inference and query engine for the Cross-Categorization model of tabular data.

Changes:

Initial Announcement on mloss.org.


Logo apsis 0.1.1

by fdiehl - March 17, 2015, 08:27:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 681 views, 135 downloads, 2 subscriptions

About: A toolkit for hyperparameter optimization for machine learning algorithms.

Changes:

Initial Announcement on mloss.org.


Logo BayesPy 0.3.2

by jluttine - March 16, 2015, 11:58:37 CET [ Project Homepage BibTeX Download ] 4876 views, 1270 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Concatenate node added
  • Unit tests for plotting fixed

Logo Hivemall 0.3

by myui - March 13, 2015, 17:08:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5621 views, 904 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 libcmaes 0.9.5

by beniz - March 9, 2015, 09:05:22 CET [ Project Homepage BibTeX Download ] 4903 views, 1038 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 ADAMS 0.4.8

by fracpete - March 4, 2015, 00:54:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10942 views, 2384 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 Mulan 1.5.0

by lefman - February 23, 2015, 21:19:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17234 views, 6666 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 ] 880 views, 159 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 ] 632 views, 144 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 ] 16287 views, 4072 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/.


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