Open Source Windows Machine Learning Software - Page 2

Machine Learning Software for Windows

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  • 1
    The Edge Machine Learning library

    The Edge Machine Learning library

    Machine learning algorithms for edge devices

    Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency, and energy. One instance of where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.These algorithms can train models for classical supervised learning problems with memory requirements that are orders of magnitude lower than other modern ML algorithms. The trained models can be loaded onto edge devices such as IoT devices/sensors, and used to make fast and accurate predictions completely offline. A tool that adapts models trained by above algorithms to be inferred by fixed point arithmetic.
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  • 2
    Weka4OC GUI for Overlapping clustering

    Weka4OC GUI for Overlapping clustering

    Weka4OC: Weka for Overlapping Clustering is a GUI extending WEKA

    This is a GUI application for learning non disjoint groups based on Weka machine learning framework. It offers a variety of learning methods, based on k-means, able to produce overlapping clusters. The application also contains an evaluation framework that calculates several external validation measures. The application offers a visualization tool to discover overlapping groups.
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  • 3

    drvq

    dimensionality-recursive vector quantization

    drvq is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. A detailed README file describes the usage of the software, including license, requirements, installation, file formats, sample data, tools, and options. With the sample data provided and the default options, it is possible to test the code immediately as a demo. DRVQ has a 2-clause BSD license. Please refer to the DRVQ software home page, the research project, or the original publication for more information. The latest code is available at github.
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  • 4
    An information extraction library implementing modern algorithms for the extraction of named entities from text.
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  • 5
    Todbot is an AI addon to the gamestool virtual machine. The aim is to create an evolving neural network topology that should be capable of creating the optimal topology and network weighting to solve any problem given it.
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