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Data Mining for Everybody Using Data Compression

Rudi Cilibrasi (853775) writes | more than 7 years ago

Programming

Rudi Cilibrasi writes "In recent years, it has become possible to get tremendous amounts of computational power cheaply on home desktops. This has opened up the field of artificial intelligence and pattern recognition research to a much wider audience. Another important synergy is the rapid growth of open source scientific software in the past five years. Only recently has the confluence of these two forces combined to create wonderfully easy new machine learning opportunities for developers everywhere. Over a year ago, we reported on Slashdot the results of new scientific research involving data compression programs. Scientists had been experimenting with language analysis, spam detection, genetic analysis, and much more. Now, the software used to do the machine learning experiments and pattern recognition has been refined to version 0.9.6 and is very near its 1.0 release. The CompLearn development team would like to take this opportunity to welcome the Slashdot community to have a look at the libcomplearn software library and see if it suits your expanding needs as software developers. Does it allow you to quickly solve the data mining and pattern recognition applications that interest you? As we get ready to release 1.0 we are particularly in need of API feedback. If you have ever been interested in artificial intelligence, statistics without calculus, or foundational math, then please enjoy our software library offering and research. We have taken great pains to make the software as easy to install as possible; if you are familiar with programs like bzip2 or gzip then you can already combine data compression programs with Neural Networks, Support Vector Machines, or other machine learning algorithms to create powerful recognition systems quickly and easily. We have used CompLearn with the open source LIBSVM library to create powerful recognizers in a variety of languages and invite you to experiment with this new type of algorithm. I look forward to seeing what sorts of applications develop."

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