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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 ] 19508 views, 3932 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 ] 9789 views, 2091 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 ] 97905 views, 16894 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 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 ] 200 views, 37 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.


Logo Probabilistic Classification Vector Machine 0.2

by fmschleif - April 29, 2015, 14:49:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 261 views, 43 downloads, 2 subscriptions

About: PCVM library a c++/armadillo implementation of the Probabilistic Classification Vector Machine.

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 ] 232 views, 45 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 ] 223 views, 54 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.


Logo KeLP 1.0.0

by kelpadmin - April 27, 2015, 16:44:36 CET [ Project Homepage BibTeX Download ] 231 views, 43 downloads, 2 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate classifiers without writing a single line of code.

Changes:

Initial Announcement on mloss.org.


Showing Items 1-10 of 580 on page 1 of 58: 1 2 3 4 5 6 Next Last