Showing 3 open source projects for "liblpsolve55.so"

View related business solutions
  • Atera all-in-one platform IT management software with AI agents Icon
    Atera all-in-one platform IT management software with AI agents

    Ideal for internal IT departments or managed service providers (MSPs)

    Atera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
    Learn More
  • Free and Open Source HR Software Icon
    Free and Open Source HR Software

    OrangeHRM provides a world-class HRIS experience and offers everything you and your team need to be that HR hero you know that you are.

    Give your HR team the tools they need to streamline administrative tasks, support employees, and make informed decisions with the OrangeHRM free and open source HR software.
    Learn More
  • 1
    VDM.net So how can I call you My Dear Project?
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2

    libSVMWrapper

    .NET wrapper for libsvm.dll on LGPL

    libSVMWrapper a wrapper for libsvm.dll What is it? libSVMWrapper is a .NET wrapper for libsvm.dll and is provided to you under LGPL license version 3. This library should work with 32bit version of libsvm.dll. Documentation for libsvm.dll Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    CRFSharp

    CRFSharp

    CRFSharp is a .NET(C#) implementation of Conditional Random Field

    CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next