Showing 2 open source projects for "vector"

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  • Dun and Bradstreet Risk Analytics - Supplier Intelligence Icon
    Dun and Bradstreet Risk Analytics - Supplier Intelligence

    Use an AI-powered solution for supply and compliance teams who want to mitigate costly supplier risks intelligently.

    Risk, procurement, and compliance teams across the globe are under pressure to deal with geopolitical and business risks. Third-party risk exposure is impacted by rapidly scaling complexity in domestic and cross-border businesses, along with complicated and diverse regulations. It is extremely important for companies to proactively manage their third-party relationships. An AI-powered solution to mitigate and monitor counterparty risks on a continuous basis, this cutting-edge platform is powered by D&B’s Data Cloud with 520M+ Global Business Records and 2B+ yearly updates for third-party risk insights. With high-risk procurement alerts and multibillion match points, D&B Risk Analytics leverages best-in-class risk data to help drive informed decisions. Perform quick and comprehensive screening, using intelligent workflows. Receive ongoing alerts of key business indicators and disruptions.
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    HermeneutiX

    HermeneutiX

    Your graphical tool for Syntactic/Semantic Structure Analysis of texts

    HermeneutiX is a tool for diagramming syntactic and semantic structures of complex (not necessarily foreign-language) texts (e.g. bible or other historical excerpts). HermeneutiX is now part of SciToS (the scientific tool set). Starting with version 2.0.0, HermeneutiX can be found on GitHub. Please check out the release summary: https://github.com/scientific-tool-set/scitos/releases For an introduction, check out this video: https://youtu.be/uQjewyG0Ad8 PS: To run a Java...
    Downloads: 1 This Week
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    Unsupervised TXT classifier

    Unsupervised TXT classifier

    Classify any two TXT documents, no training required - JAVA

    ...Then, the summarizer gets trained with A to summarize a document B, and vice versa. This extracts a relevant structure for both documents (and thus avoids the over-training) which are then compared using the Vector-Space analysis to give a range of belonging of one document to another (and thus avoids the shortage of information). This method can be used to create the user-defined classes by merging texts of certain categories and then to calculate the relevant distances between the documents, but this is not necessary.
    Downloads: 0 This Week
    Last Update:
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