Showing 7 open source projects for "linux full install"

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  • 1
    GROBID

    GROBID

    A machine learning software for extracting information

    GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby. In 2011 the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such. Header extraction and parsing from article in PDF format. The...
    Downloads: 4 This Week
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  • 2
    Easy Machine Learning

    Easy Machine Learning

    Easy Machine Learning is a general-purpose dataflow-based system

    Machine learning algorithms have become the key components in many big data applications. However, the full potential of machine learning is still far from being realized because using machine learning algorithms is hard, especially on distributed platforms such as Hadoop and Spark. The key barriers come from not only the implementation of the algorithms themselves but also the processing for applying them to real applications which often involve multiple steps and different algorithms. Our...
    Downloads: 0 This Week
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  • 3

    LAML:Linear Algebra and Machine Learning

    A stand-alone Java library for linear algebra and machine learning

    LAML is a stand-alone pure Java library for linear algebra and machine learning. The goal is to build efficient and easy-to-use linear algebra and machine learning libraries. The reason why linear algebra and machine learning are built together is that full control of the basic data structures for matrices and vectors is required to have fast implementation for machine learning methods. Additionally, LAML provides a lot of commonly used matrix functions in the same signature to MATLAB, thus...
    Downloads: 0 This Week
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  • 4
    JCLAL

    JCLAL

    A Java Class Library for Active Learning

    JCLAL is a general purpose framework developed in Java for the active learning research area. JCLAL framework is open source software and it is distributed under the GNU general public license. It is constructed with a high-level software environment, with a strong object oriented design and use of design patterns, which allow to the developers reuse, modify and extend the framework. If you want to refer to JCLAL in a publication, please cite the following JMLR paper. The full citation...
    Downloads: 0 This Week
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    Run Any Workload on Compute Engine VMs

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  • 5

    GA-EoC

    GeneticAlgorithm-based search for Heterogeneous Ensemble Combinations

    In data classification, there are no particular classifiers that perform consistently in every case. This is even worst in case of both the high dimensional and class-imbalanced datasets. To overcome the limitations of class-imbalanced data, we split the dataset using a random sub-sampling to balance them. Then, we apply the (alpha,beta)-k feature set method to select a better subset of features and combine their outputs to get a consolidated feature set for classifier training. To...
    Downloads: 1 This Week
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  • 6

    Chordalysis

    Log-linear analysis (data modelling) for high-dimensional data

    ===== Project moved to https://github.com/fpetitjean/Chordalysis ===== Log-linear analysis is the statistical method used to capture multi-way relationships between variables. However, due to its exponential nature, previous approaches did not allow scale-up to more than a dozen variables. We present here Chordalysis, a log-linear analysis method for big data. Chordalysis exploits recent discoveries in graph theory by representing complex models as compositions of triangular structures,...
    Downloads: 0 This Week
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  • 7
    RoboBeans is an interface to the "Robocup 2D Soccer Simulation Server" that allows developers to write Robocup teams\agents concentrating on behaviour and AI without having to worry about syntax of communication or network issues.
    Downloads: 0 This Week
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