Showing 4 open source projects for "learning classifier system"

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  • 1
    NLP.js

    NLP.js

    An NLP library for building bots

    NLP.js is an NLP library for building bots, with entity extraction, sentiment analysis, automatic language identifier, and much more. "NLP.js" is a general natural language utility for nodejs. Search the best substring of a string with less Levenshtein distance to a given pattern. Get stemmers and tokenizers for several languages. Sentiment Analysis for phrases (with negation support). Named Entity Recognition and management, multi-language support, and acceptance of similar strings, so the...
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  • 2
    fastText

    fastText

    Library for fast text classification and representation

    ...Models can later be reduced in size to even fit on mobile devices. ext classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool. The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. ...
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  • 3
    Intel neon

    Intel neon

    Intel® Nervana™ reference deep learning framework

    ...The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. The Intel Math Kernel Library takes advantages of the parallelization and vectorization capabilities of Intel Xeon and Xeon Phi systems. When hyperthreading is enabled on the system, we recommend the following KMP_AFFINITY setting to make sure parallel threads are 1:1 mapped to the available physical cores.
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  • 4
    Phrasal

    Phrasal

    Statistical phrase-based machine translation system

    Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java. At its core, it provides much the same functionality as the core of Moses. Distinctive features include: providing an easy to use API for implementing new decoding model features, the ability to translating using phrases that include gaps (Galley et al. 2010), and conditional extraction of phrase-tables and lexical reordering models. Developed by The Natural Language Processing Group...
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