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Logo JMLR dlib ml 19.3

by davis685 - February 22, 2017, 04:37:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 170538 views, 27130 downloads, 5 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 a number of new features, most notably new deep learning tools including a state-of-the-art face recognition example using dlib's deep learning API. See http://dlib.net/dnn_face_recognition_ex.cpp.html for an introduction.


Logo ADENINE 0.1.4

by samuelefiorini - February 17, 2017, 14:50:49 CET [ Project Homepage BibTeX Download ] 1378 views, 307 downloads, 2 subscriptions

About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, unsupervised feature learning and clustering tasks.

Changes:
  • Adenine can now distribute the execution of its pipelines on multiple machines via MPI
  • kNN data imputing strategy is now implemented
  • added python 2.7 and 3.5 support
  • stability improved and bug fixed

Logo scikit multilearn 0.0.4

by niedakh - February 15, 2017, 21:11:40 CET [ Project Homepage BibTeX Download ] 1475 views, 361 downloads, 3 subscriptions

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

Changes:

*kNN classifiers support sparse matrices properly support for the new model_selection API from scikit-learn extended graph-based label space clusteres to allow taking probability of a label occuring alone into consideration compatible with newest graphtool support the case when meka decides that an observation doesn't have any labels assigned HARAM classifier provided by Fernando Benitez from University of Konstanz predict_proba added to problem transformation classifiers ported to python 3


Logo Bagging PCA Hashing 1.0

by openpr_nlpr - February 6, 2017, 10:38:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 425 views, 38 downloads, 3 subscriptions

About: The proposed hashing algorithm leverages the bootstrap sampling idea and integrates it with PCA, resulting in a new projection method called Bagging PCA Hashing.

Changes:

Initial Announcement on mloss.org.


Logo Online Sketching Hashing 1.0

by openpr_nlpr - February 6, 2017, 10:36:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 378 views, 40 downloads, 3 subscriptions

About: This is an online hashing algorithm which can handle the stream data with low computational cost.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-e1071 1.6-7

by r-cran-robot - February 1, 2017, 00:00:07 CET [ Project Homepage BibTeX Download ] 34442 views, 6875 downloads, 3 subscriptions

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

Changes:

Fetched by r-cran-robot on 2017-02-01 00:00:07.859922


Logo r-cran-CoxBoost 1.4

by r-cran-robot - February 1, 2017, 00:00:06 CET [ Project Homepage BibTeX Download ] 31802 views, 6085 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 2017-02-01 00:00:06.477910


Logo r-cran-Boruta 5.2.0

by r-cran-robot - February 1, 2017, 00:00:04 CET [ Project Homepage BibTeX Download ] 23587 views, 4777 downloads, 2 subscriptions

About: Wrapper Algorithm for All Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2017-02-01 00:00:04.943260


Logo revrand 1.0.0

by dsteinberg - January 29, 2017, 04:33:54 CET [ Project Homepage BibTeX Download ] 10320 views, 2029 downloads, 3 subscriptions

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About: A library of scalable Bayesian generalised linear models with fancy features

Changes:
  • 1.0 release!
  • Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
  • Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
  • Random state can be set for all random objects
  • Numerous small improvements to make revrand production ready
  • Final report
  • Documentation improvements

Logo r-cran-biglasso 1.3-3

by r-cran-robot - January 24, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 425 views, 57 downloads, 2 subscriptions

About: Extending Lasso Model Fitting to Big Data

Changes:

Initial Announcement on mloss.org by r-cran-robot


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