Skip to content
MIT Press
  • MIT Press
  • Books
    • Column
      • View all subjects
      • New releases
      • Catalogs
      • Textbooks
      • Series
      • Awards
    • Column
      • Authors
      • Distributed presses
      • The MIT Press Reader
      • Podcasts
      • Collections
    • Column
      • MIT Press Direct

        MIT Press Direct is a distinctive collection of influential MIT Press books curated for scholars and libraries worldwide.

        • Learn more
  • Journals
    • column
      • Journals all topics
      • Economics
      • International Affairs, History, & Political Science
    • column
      • Arts & Humanities
      • Science & Technology
      • Open access
    • column
      • MIT Press journals

        MIT Press began publishing journals in 1970 with the first volumes of Linguistic Inquiry and the Journal of Interdisciplinary History. Today we publish over 30 titles in the arts and humanities, social sciences, and science and technology.

        • Learn more
  • Open Access
    • column
      • Open access at the MIT Press
      • Open access books
      • Open access journals
    • column
      • Direct to Open
      • MIT Open Publishing Services
      • MIT Press Open on PubPub
    • Column
      • Open access

        The MIT Press has been a leader in open access book publishing for over two decades, beginning in 1995 with the publication of William Mitchell’s City of Bits, which appeared simultaneously in print and in a dynamic, open web edition.

        • Learn more
  • Resources
    • column
      • Current authors
      • Prospective authors
      • Instructors
    • column
      • Media inquiries
      • Booksellers
      • Rights and permissions
    • column
      • Resources

        Collaborating with authors, instructors, booksellers, librarians, and the media is at the heart of what we do as a scholarly publisher. If you can’t find the resource you need here, visit our contact page to get in touch.

        • Learn more
  • Give
  • About
    • Column
      • About
      • Jobs
      • Internships
      • MIT Press Editorial Board
      • MIT Press Management Board
      • Our MIT story
    • Column
      • Catalogs
      • News
      • Events
      • Conferences
      • Bookstore
    • Column
      • The MIT Press

        Established in 1962, the MIT Press is one of the largest and most distinguished university presses in the world and a leading publisher of books and journals at the intersection of science, technology, art, social science, and design.

        • Learn more
  • Contact Us
Newsletter
MIT Press
Newsletter

Books

    Authors

      On the site

        • Home
        • Neural Information Processing series
        • computers
        • Large-Scale Kernel Machines
        Large-Scale Kernel Machines

        Neural Information Processing series

        Large-Scale Kernel Machines

        Edited by Léon Bottou, Olivier Chapelle, Dennis DeCoste and Jason Weston

        • Hardcover

        408 pp., 8 x 10 in, 116 figures, 43 tables

        • Hardcover
        • 9780262026253
        • Published: August 17, 2007
        • Publisher: The MIT Press

        $50.00

        • Penguin Random House
        • Amazon
        • Barnes and Noble
        • Bookshop.org
        • Indiebound
        • Indigo
        • Books a Million

        Other Retailers:

        • Amazon.co.uk
        • Blackwells
        • Bookshop.org
        • Foyles
        • Hive
        • Waterstones
        • Penguin Random House
        • Amazon
        • Barnes and Noble
        • Bookshop.org
        • Indiebound
        • Indigo
        • Books a Million
        • Request permissions
        • Description
        • Author(s)
        • Resources

        Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.

        Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.

        Contributors
        Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov

        Léon Bottou is a Research Scientist at NEC Labs America.

        Olivier Chapelle is Senior Research Scientist in Machine Learning at Yahoo.

        Dennis DeCoste is with Microsoft Research.

        Jason Weston is a Research Scientist at NEC Labs America.

        Sample Chapter

        Preface

        Index

        Related Books

        Physically Based Rendering
        The Developmental Organization of Robot Behavior
        Essentials of Compilation
        Computing and Technology Ethics
        The Computer Music Tutorial
        Introduction to Autonomous Robots
        Microprediction
        High-Performance Big Data Computing
        Introduction to Algorithms
        An Experiential Introduction to Principles of Programming Languages
        logo
        • Column 1
          • Books
          • Journals
          • The MIT Press Reader
          • Podcasts
          • Imprints
        • Column 2
          • The MIT Press
            • About
            • Bookstore
            • Catalogs
            • Conferences
            • Press Editorial Board
            • Jobs
            • Internships
            • Press Management Board
            • News
            • Staff
            • Code of Conduct
            • Give
        • Column 3
          • Site Help
            • Accessibility
            • FAQ
            • Our eBooks
            • Privacy Policy
            • Terms of Use
        • Column 4
          • Resources
            • Current Authors
            • Prospective Authors
            • Booksellers
            • Instructors
            • Rights and Permissions
            • Media Inquiries
            • MIT Discounts
        • Column 5
          • Digital
            • CogNet
            • Digital Partners and Products
            • Knowledge Futures Group
            • MIT Press Direct
        • Global

          One Broadway 12th Floor Cambridge, MA 02142

        • Contact

        Connect

        © 2023 MIT Press. All Rights Reserved.

        Powered by Supadu