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  • Varchas 11:28 am on June 4, 2010 Permalink | Log in to leave a Comment  

    Nearest Neighbor Classifier Bug Fixed 

    There was a bug in the windows version of the nearest neighbor classifier that has been fixed. Specifically, the program would die quietly after performing nearest neighbors whilst reading the input labels. This has been fixed.

     
  • Varchas 12:54 pm on February 17, 2010 Permalink | Log in to leave a Comment  

    Announcing Hadoop Bootstrapped SVM 

    We are pleased to announce a bootstrapped version of the Analytics1305 Support Vector Machine, available on Amazon Ec2, which utilizes the open source implementation of Google’s map-reduce technology embodied as the Apache Hadoop project.

    Bootstrapping is a method employed in machine learning to improve model accuracy and stability (confidence in results). Support Vector Machines are the best off-the-shelf classifier technology available today. The combination of the two makes a powerful and versatile classifier for most types of data and we have made every effort to make the whole analysis process from raw data to classification results as simple as possible. For more info on the beta release please refer to our documentation here.

     
  • Varchas 4:18 pm on December 25, 2009 Permalink | Log in to leave a Comment  

    SVM Classifier Released On Amazon Ec2 

    We are very excited to announce a Support Vector Machine (SVM) classification algorithm available on our free public cloud machine as well as free public AMI. It is a 2-class classifier which implements the SMO optimization algorithm. The salient features are as follows:

    • Implements 2 class Sequential Minimal Optimization (SMO)
    • Uses 1305 file formant and thus can handle categorical and sparse data formats
    • Generated models can be saved and used for classification at any time
    • Offers 2nd order working set selection and caching techniques for speed

    Our benchmarks beat the popular LibSVM and SVM-Light tools for standard problems from the UCI machine learning datasets repository. Please find documentation and samples on how to run the SVM here.

     
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