Scaling Up Machine Learning Parallel and Distributed Approaches

Bekkerman, Ron & Mikhail Bilenko & John Langford

ISBN 10: 0521192242 / ISBN 13: 9780521192248

Editore: Cambridge University Press, 2011

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Light rubbing and toning overall and some light scratches. Interior pages clean and unmarked. A tight and clean copy. Photos upon request. International shipping billed at cost.; 4to 11" - 13" tall; 492 pages.

Codice articolo 89027

Dati bibliografici

• Titolo: Scaling Up Machine Learning Parallel and Distributed Approaches
• Editore: Cambridge University Press
• Data di pubblicazione: 2011
• Lingua: Inglese
• ISBN 10: 0521192242
• ISBN 13: 9780521192248
• Rilegatura: Hardcover
• Edizione: First Edition.
• Condizione: Very Good+
• Condizione della sovraccoperta: No Dust Jacket
• Cataloghi dei venditori:
  • Mathematics
  • University Press
  • Education
  • Reference

Descrizione articolo

Riassunto

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Informazioni sugli autori

Ron Bekkerman is a computer engineer and scientist whose experience spans across disciplines from video processing to business intelligence. Currently a senior research scientist at LinkedIn, he previously worked for a number of major companies including Hewlett-Packard and Motorola. Bekkerman's research interests lie primarily in the area of large-scale unsupervised learning. He is the corresponding author of several publications in top-tier venues, such as ICML, KDD, SIGIR, WWW, IJCAI, CVPR, EMNLP and JMLR.

Mikhail Bilenko is a researcher in the Machine Learning and Intelligence group at Microsoft Research. His research interests center on machine learning and data mining tasks that arise in the context of large behavioral and textual datasets. Bilenko's recent work has focused on learning algorithms that leverage user behavior to improve online advertising. His papers have been published at KDD, ICML, SIGIR, and WWW among other venues, and he has received best paper awards from SIGIR and KDD.

John Langford is a computer scientist working as a senior researcher at Yahoo! Research. Previously, he was affiliated with the Toyota Technological Institute and IBM T. J. Watson Research Center. Langford's work has been published at conferences and in journals including ICML, COLT, NIPS, UAI, KDD, JMLR and MLJ. He received the Pat Goldberg Memorial Best Paper Award, as well as best paper awards from ACM EC and WSDM. He is also the author of the popular machine learning weblog, hunch.net.