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Logo Cognitive Foundry 3.4.1

by Baz - May 13, 2015, 06:55:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19682 views, 3228 downloads, 3 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

Changes:
  • General:
    • Updated MTJ to version 1.0.2 and netlib-java to 1.1.2.
    • Updated XStream to version 1.4.8.
  • Common:
    • Fixed issue in VectorUnionIterator.
  • Learning:
    • Added Alternating Least Squares (ALS) Factorization Machine training implementation.
    • Fixed performance issue in Factorization Machine where linear component was not making use of sparsity.
    • Added utility function to sigmoid unit.

Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 6723 views, 1299 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:
  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version


About: This MATLAB package provides the LOMO feature extraction and the XQDA metric learning algorithms proposed in our CVPR 2015 paper. It is fast, and effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-CoxBoost 1.4

by r-cran-robot - May 1, 2015, 00:00:04 CET [ Project Homepage BibTeX Download ] 19906 views, 4019 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 2015-05-01 00:00:04.536435


Logo r-cran-Boruta 4.0.0

by r-cran-robot - May 1, 2015, 00:00:04 CET [ Project Homepage BibTeX Download ] 10192 views, 2173 downloads, 2 subscriptions

About: Wrapper Algorithm for All-Relevant Feature Selection

Changes:

Fetched by r-cran-robot on 2015-05-01 00:00:04.178596


Logo JMLR dlib ml 18.15

by davis685 - April 30, 2015, 03:49:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 100770 views, 17281 downloads, 4 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 the least-squares policy iteration algorithm, a tool for plotting 3D point clouds, and a few bug fixes and usability improvements.


Logo Nilearn 0.1.2

by goulagman - April 29, 2015, 16:16:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 542 views, 122 downloads, 3 subscriptions

About: Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Changes:

Initial Announcement on mloss.org.


About: FAST is an implementation of Hidden Markov Models with Features. It allows features to modify both emissions and transition probabilities.

Changes:

Initial Announcement on mloss.org.


Logo MIPS, The migrant implementation system 1.0

by thomasfannes - April 28, 2015, 15:07:05 CET [ Project Homepage BibTeX Download ] 462 views, 106 downloads, 3 subscriptions

About: MIPS is a software library for state-of-the-art graph mining algorithms. The library is platform independent, written in C++(03), and aims at implementing generic and efficient graph mining algorithms.

Changes:

description update


Logo streamDM 0.0.1

by abifet - April 28, 2015, 12:34:00 CET [ Project Homepage BibTeX Download ] 420 views, 131 downloads, 1 subscription

About: streamDM is a new open source data mining and machine learning library, designed on top of Spark Streaming, an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of data streams.

Changes:

Initial Announcement on mloss.org.


Showing Items 11-20 of 583 on page 2 of 59: Previous 1 2 3 4 5 6 7 Next Last