The September 2010 Release of the Analytics1305 Machine Learning Library is now available for download. The following new features are available:
QuicSVD: A new addition to our library; it is a fast singular value decomposition method for dimensionality reduction.
Support Vector Machines: Added polynomial kernel to the list of non-linear kernels.
As usual there have been bug fixes and updates to the online documentation.
Analytics1305 Development Team.
The July 2010 Release of the Analytics1305 Machine Learning Library is now available for download. As promised the following new features are available:
Additionally we have included a Support Vector Machine algorithm which uses the Sequential Minimal Optimization algorithm. The binaries support the linear and non-linear Gaussian kernel.
Analytics1305 Development Team.
Dear Friends,
We are pleased to announce the July 2010 release of the Analytics1305 Machine Learning Library. The release will be available for download on Friday the second of July. This release will include the following new features:
As always our releases include bug fixes as we isolate them.
Dear Friends,
We have received numerous requests from our users to make our algorithms available for downloading and we are pleased to announce our first (beta) release. Users can now download binaries for our machine learning algorithms natively compiled for the following operating systems:
The current release only supports 32 bit platforms with 64 bit planned for a future release. As you well know our algorithms are orders of magnitude faster than those available on R, Weka and SPSS. For the current release the following binaries are available:
Our algorithms support both dense and sparse data along with progressive modes. We have also revised our documentation with a renewed focus on usage examples. As always we support the release with our forums.
Here is a link to the download page.
We are pleased to announce fast implementations of linear regression, singular value decomposition, and non-negative matrix factorization. They are now available on Amazon Ec2.
Our implementation of linear regression supports the variable elimination algorithm based on variance inflation factor and stepwise regression based on the AIC criterion.
Our implementation of singular value decomposition uses the Quic-SVD algorithm that uses a tree structure called the cosine tree.
Our implementation of non-negative matrix factorization supports the factorization based on the specified sparsity level.
For more info on the beta release please refer to our documentation here.
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.
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:
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.
If you find the instructions hard you can always take a look at the following videos about how to launch an Amazon EC2 AMI and most important, how to TERMINATE it once you are done, otherwise you will get unwanted charges.
The current public AMI is:
ami-d70fe1be
There is no “root” access to our AMI’s. The user name is “a1305″ and you can use your own Amazon Ec2 account’s private key to ssh. Commonly the command will look like:
ssh -i <path to private key> a1305@<Public DNS of launched cloud machine>
Currently we only support 32-bit configurations. Please use the m1.small type machine.
(More …)
First of all, very nice job. Your library and AMI will be very helpful for me. So thank you.
But I wonder why there’s no root access? is there a reason for that?
I need to mount an EBS volume, and that requires root!
Since some of you might want to quickly test the 1305 library, we have launched an Amazon EC2 machine, so that you can connect and test our algorithms. The ssh command is:
ssh a1305@ec2-67-202-56-237.compute-1.amazonaws.com
and use sixtyfive as the password.
Notice that this is a public machine that many people might be using. So make sure you don’t upload sensitive data. It is also recommended that you create a folder within the home directory “/home/a1305/<your unique directory name here>” and work from within this folder. You might also experience slow performance because other people might be submitting jobs. If you need a machine that you have exclusive access follow these directions.
For help and support either send emails to cloud@analytics1305.com or post your question on the forums.
UPDATE: We have bought this machine down since we are now providing binaries you can download. Thanks to those who used it!
- Analytics1305 Developer Team
TheCloudMarket 12:59 pm on February 26, 2010 Permalink
Hi,
We are indexing your AMI at our site.
http://thecloudmarket.com/owner/854385305035–analytics1305
You can update the information about it and yourself by following the “claiming” process.
http://thecloudmarket.com/about.html#/claiming
Cheers