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Logo r-cran-C50 0.1.0-24

by r-cran-robot - March 8, 2015, 00:00:00 CET [ Project Homepage BibTeX Download ] 3947 views, 936 downloads, 0 subscriptions

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

Changes:

Fetched by r-cran-robot on 2015-05-01 00:00:04.322186


Logo ADAMS 0.4.8

by fracpete - March 4, 2015, 00:54:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10448 views, 2316 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 ] 16788 views, 6587 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 ] 725 views, 123 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 ] 519 views, 118 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 ] 15985 views, 4020 downloads, 6 subscriptions

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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/.


Logo Auto encoder Based Data Clustering Toolkit 1.0

by openpr_nlpr - February 10, 2015, 08:30:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 659 views, 125 downloads, 2 subscriptions

About: The auto-encoder based data clustering toolkit provides a quick start of clustering based on deep auto-encoder nets. This toolkit can cluster data in feature space with a deep nonlinear nets.

Changes:

Initial Announcement on mloss.org.


Logo Histogram of Oriented Gradient 1.0

by openpr_nlpr - February 10, 2015, 08:27:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 556 views, 109 downloads, 2 subscriptions

About: This is an exact implementation of Histogram of Oriented Gradient as mentioned in the paper by Dalal.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Information Theoretical Estimators 0.61

by szzoli - February 8, 2015, 14:04:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 64005 views, 13358 downloads, 2 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

Changes:
  • Explicit additive constant computation in generalized kNN based Renyi entropy estimators: enhancement suggestion has been added.

  • Analytical value computation of the exponentiated Jensen-Renyi kernel-2: simplified.


Logo JMLR SHOGUN 4.0.0

by sonne - February 5, 2015, 09:09:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 92658 views, 12925 downloads, 6 subscriptions

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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.

Changes:

This release features the work of our 8 GSoC 2014 students [student; mentors]:

  • OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
  • Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
  • Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
  • Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
  • Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
  • Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
  • Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
  • Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]

It also contains several cleanups and bugfixes:

Features

  • New Shogun project description [Heiko Strathmann]
  • ID3 algorithm for decision tree learning [Parijat Mazumdar]
  • New modes for PCA matrix factorizations: SVD & EVD, in-place or reallocating [Parijat Mazumdar]
  • Add Neural Networks with linear, logistic and softmax neurons [Khaled Nasr]
  • Add kernel multiclass strategy examples in multiclass notebook [Saurabh Mahindre]
  • Add decision trees notebook containing examples for ID3 algorithm [Parijat Mazumdar]
  • Add sudoku recognizer ipython notebook [Alejandro Hernandez]
  • Add in-place subsets on features, labels, and custom kernels [Heiko Strathmann]
  • Add Principal Component Analysis notebook [Abhijeet Kislay]
  • Add Multiple Kernel Learning notebook [Saurabh Mahindre]
  • Add Multi-Label classes to enable Multi-Label classification [Thoralf Klein]
  • Add rectified linear neurons, dropout and max-norm regularization to neural networks [Khaled Nasr]
  • Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]
  • Add support for arbitrary acyclic graph-structured neural networks [Khaled Nasr]
  • Add CART algorithm for classification and regression using decision trees [Parijat Mazumdar]
  • Add CHAID algorithm for multiclass classification and regression using decision trees [Parijat Mazumdar]
  • Add Convolutional Neural Networks [Khaled Nasr]
  • Add Random Forests algorithm for ensemble learning using CART [Parijat Mazumdar]
  • Add Restricted Botlzmann Machines [Khaled Nasr]
  • Add Stochastic Gradient Boosting algorithm for ensemble learning [Parijat Mazumdar]
  • Add Deep contractive and denoising autoencoders [Khaled Nasr]
  • Add Deep belief networks [Khaled Nasr]

Bugfixes

  • Fix reference counting bugs in CList when reference counting is on [Heiko Strathmann, Thoralf Klein, lambday]
  • Fix memory problem in PCA::apply_to_feature_matrix [Parijat Mazumdar]
  • Fix crash in LeastAngleRegression for the case D greater than N [Parijat Mazumdar]
  • Fix memory violations in bundle method solvers [Thoralf Klein]
  • Fix fail in library_mldatahdf5.cpp example when http://mldata.org is not working properly [Parijat Mazumdar]
  • Fix memory leaks in Vowpal Wabbit, LibSVMFile and KernelPCA [Thoralf Klein]
  • Fix memory and control flow issues discovered by Coverity [Thoralf Klein]
  • Fix R modular interface SWIG typemap (Requires SWIG >= 2.0.5) [Matt Huska]

Cleanup and API Changes

  • PCA now depends on Eigen3 instead of LAPACK [Parijat Mazumdar]
  • Removing redundant and fixing implicit imports [Thoralf Klein]
  • Hide many methods from SWIG, reducing compile memory by 500MiB [Heiko Strathmann, Fernando Iglesias, Thoralf Klein]

Showing Items 31-40 of 579 on page 4 of 58: Previous 1 2 3 4 5 6 7 8 9 Next Last