Skip to main content
SpringerLink
Log in
Book cover

Advanced Lectures on Machine Learning

Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures

  • Textbook
  • © 2003

Overview

Editors:
  1. Shahar Mendelson

    1. RSISE, The Australian National University, Canberra, Australia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Alexander J. Smola

    1. Research School for Information Sciences and Engineering, The Australian National University, Canberra, Australia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2600)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (8 chapters)

  1. Front Matter

    Pages I-X
    Download chapter PDF

  2. A Few Notes on Statistical Learning Theory

    • Shahar Mendelson
    Pages 1-40

  3. A Short Introduction to Learning with Kernels

    • Bernhard Schölkopf, Alexander J. Smola
    Pages 41-64

  4. Bayesian Kernel Methods

    • Alexander J. Smola, Bernhard Schölkopf
    Pages 65-117

  5. An Introduction to Boosting and Leveraging

    • Ron Meir, Gunnar Rätsch
    Pages 118-183

  6. An Introduction to Reinforcement Learning Theory: Value Function Methods

    • Peter L. Bartlett
    Pages 184-202

  7. Learning Comprehensible Theories from Structured Data

    • J.W. Lloyd
    Pages 203-225

  8. Algorithms for Association Rules

    • Markus Hegland
    Pages 226-234

  9. Online Learning of Linear Classifiers

    • Jyrki Kivinen
    Pages 235-257

  10. Back Matter

    Pages 259-259
    Download chapter PDF
Back to top

Keywords

About this book

Machine Learning has become a key enabling technology for many engineering applications and theoretical problems alike. To further discussions and to dis- minate new results, a Summer School was held on February 11–22, 2002 at the Australian National University. The current book contains a collection of the main talks held during those two weeks in February, presented as tutorial chapters on topics such as Boosting, Data Mining, Kernel Methods, Logic, Reinforcement Learning, and Statistical Learning Theory. The papers provide an in-depth overview of these exciting new areas, contain a large set of references, and thereby provide the interested reader with further information to start or to pursue his own research in these directions. Complementary to the book, a recorded video of the presentations during the Summer School can be obtained at http://mlg. anu. edu. au/summer2002 It is our hope that graduate students, lecturers, and researchers alike will ?nd this book useful in learning and teaching Machine Learning, thereby continuing the mission of the Summer School. Canberra, November 2002 Shahar Mendelson Alexander Smola Research School of Information Sciences and Engineering, The Australian National University Thanks and Acknowledgments We gratefully thank all the individuals and organizations responsible for the success of the workshop.

Editors and Affiliations

  • RSISE, The Australian National University, Canberra, Australia

    Shahar Mendelson

  • Research School for Information Sciences and Engineering, The Australian National University, Canberra, Australia

    Alexander J. Smola

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation