Cloud1305- Machine Learning On Demand

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Analytics1305 has built a sophisticated library of machine learning and statistics methods that have been designed from the ground up to be scalable and extremely fast. It contains many of the latest and most advanced algorithms available for the common data analysis tasks of classification, regression, clustering etc. Combining our library with the Amazon EC2 service has given us the opportunity to facilitate its public use. Within a few minutes and a free EC2 account later the analyst can launch multiple machines on the cloud with our libraries pre-installed. Then the data can be uploaded and the required analysis task run. We will provide free support through our documentation, blogs and forums.

Details

Company: Analytics1305 LLC
Inquiry e-mail address: cloud@analytics1305.com
Amazon Web Services Used: Amazon Elastic Compute Cloud (Beta)
Solution URL: http://www.analytics1305.com/cloud/index.php
Audience: Developers
Pricing: Free of charge
How does this application use Amazon Web Services?: We have public AMI's with our machine learning library installed. Users can run a variety of machine learning algorithms on their data.
Created On: February 18, 2010 4:11 AM GMT
Last Updated: February 23, 2010 6:48 PM GMT
List of Algorithms
  1. All Neighbors beta*

    1. Nearest/Furthest
    2. K-neighbors/Range-neighbors
    3. Exact/Approximate
    4. Batch/Progressive
    5. Single/Dual tree
    6. Euclidean/Weighted Euclidean
    7. Sparse/Dense/Categorical


  2. Kernel Density Estimation beta*

    1. Gaussian/Epanechnikof kernel
    2. Single/Dual tree
    3. Exact/Approximate
    4. Batch/Progressive
    5. Euclidean/Weighted Euclidean
    6. Sparse/Dense/Categorical


  3. K-means beta*

    1. Tree/Naive
    2. Euclidean/Weighted Euclidean
    3. Sparse/Dense/Categorical


  4. Support Vector Machines beta*

    1. Sparse/Dense/Categorical
    2. Gaussian/Polynomial kernel
    3. Bootstrap (map-reduce) version


  5. More to come, subscribe to our newsletter to get updates
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