Data Mining Lab at POSTECH. ACM Data Mining SIG: Ted Dunning. Large-Scale Machine Learning: Parallelism and Massive Datasets. Friday December 11th from 7:30AM to 6:30PM Hilton at Whistler in the Mt. Currie North Room Physical and economic limitations have forced computer architecture towards parallelism and away from exponential frequency scaling. Meanwhile, increased access to ubiquitous sensing and the web has resulted in an explosion in the size of machine learning tasks. Prior NIPS workshops have focused on the topic of scaling machine learning, which remains an important developing area. While we are interested in a wide range of topics associated with large-scale, parallel learning, the following list provides a flavor of some of the key topics: The challenges and opportunities of large scale parallel machine learning are relevant to a wide range of backgrounds and interests.
Submissions are solicited for the workshop to be held on December 11th / 12th 2009 at this year's NIPS workshop session in Whistler, Canada. Parallel exact inference on multi-core processors Viktor K. K. San Francisco Bay Area ACM , Archive » 2009 ACM Silicon Valley D. LIWC - Linguistic Inquiry and Word Count. Regressive Imagery Dictionary. Download WordStat versions of the RID English version (created by Colin Martindale) French version (translated by Robert Hogenraad) Portuguese version (translated by Tito Cardoso e Cunha, Brigitte Detry, and Robert Hogenraad) Swedish version (translated by Torsten Norlander, Moira Linnarud, Marika Kjellén-Simes, and Robert Hogenraad) German version (translated by Renate Delphendahl) Hungarian version (translated by Tibor Pólya and Levente Szász) Latin version (translated by Ron Newbold) Russian version (translated by Leonid Dorfman) - Under development! Installation Instruction Extract the content of the zip file into the WordStat Dictionary folder (by default: My Documents\My Provalis Research Projects\Dict).
Most versions of the dictionary come in two files, the main .CAT file includes the various categorization of words, while the .EXC dictionary handles exceptions by excluding specific word forms. Dictionary information Selected references Goldstein, K. (1939). West, A. West, A. The Britney Spears Problem. Tracking who's hot and who's not presents an algorithmic challenge Brian Hayes Back in 1999, the operators of the Lycos Internet portal began publishing a weekly list of the 50 most popular queries submitted to their Web search engine. Britney Spears—initially tagged a "teen songstress," later a "pop tart"—was No. 2 on that first weekly tabulation. She has never fallen off the list since then—440 consecutive appearances when I last checked. Other perennials include Pamela Anderson and Paris Hilton.
What explains the enduring popularity of these celebrities, so famous for being famous? That's a fascinating question, and the answer would doubtless tell us something deep about modern culture. One challenging aspect of this task is simply coping with the volume of data. In the past few years the tracking of hot topics has itself become a hot topic in computer science. Much of the new interest in stream algorithms is inspired by the Internet, where streams of many kinds flow copiously.
The CRM114 Discriminator - The Controllable Regex Mutilator. Xifeng Yan - Home. Very sparse random projections. Rajeev Motwani. Statistical Data Mining Tutorial. Advertisment: In 2006 I joined Google. We are growing a Google Pittsburgh office on CMU's campus. We are hiring creative computer scientists who love programming, and Machine Learning is one the focus areas of the office. We're also currently accepting resumes for Fall 2008 intenships. If you might be interested, feel welcome to send me email: awm@google.com .
The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms. These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. I hope they're useful (and please let me know if they are, or if you have suggestions or error-corrections). KDnuggets: Data Mining, Web Mining, and Kn.