Showing 35 open source projects for "machine learning python"

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  • 1
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  • 2

    CvHMM

    Discrete Hidden Markov Models based on OpenCV

    This project (CvHMM) is an implementation of discrete Hidden Markov Models (HMM) based on OpenCV. It is simple to understand and simple to use. The Zip file contains one header for the implementation and one main.cpp file for a demonstration of how it works. Hope it becomes useful for your projects.
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  • 3
    usefully codes for my personal reaserch in machine learning, so maybe useful to other people too.
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  • 4
    This e-book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It focuses on Evolutionary Computation but also discusses other apporaches like Simulated Annealing and Extremal Optimization.
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  • 5
    Este proyecto constituye una adaptacion y mejora del codigo ANFIS de dominio público de Roger Jang. / This project is an adaptation and improvement of the original public domain ANFIS code of Roger Jang.
    Downloads: 1 This Week
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  • 6
    Maui is a multi-purpose automatic topic indexing algorithm. Given a document, Maui automatically identifies its topics. Depending on the task topics are tags, keywords, keyphrases, vocabulary terms, descriptors or Wikipedia titles.
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  • 7
    Overhead Imagery Research Data Set (OIRDS) - an annotated data library & tools to aid in the development of computer vision algorithms
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  • 8
    KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
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  • 9
    Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other
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  • 10
    DBNL

    DBNL

    Dynamic Bayesian Network Library

    DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables.
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