Connectionists: Book annoucement: Large Scale Kernel Machines

[Leon Bottou]

Dear colleagues,

We would like to announce a new book:

  Large-scale Kernel Machines
  MIT Press, 2007.

  http://mitpress.mit.edu/9780262026253/.
  http://leon.bottou.org/papers/lskm-2007

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 training kernel machines from large scale datasets, 
with detailed descriptions of algorithms and experiments 
carried out on realistically large datasets. 
 
After a detailed description of state-of-the-art kernel 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.

Best regards,

- Leon Bottou, NEC Labs America, Princeton, NJ,
  Olivier Chapelle, Yahoo! Research, Santa Clara, CA,
  Dennis Decoste, Microsoft, Redmond, WA, and,
  Jason Weston, NEC Labs America, Princeton, NJ.



Contents:

 1. Support Vector Machine Solvers – Bottou and Lin
 2. Training a Support Vector Machine in the Primal – Chapelle
 3. Fast Kernel Learning with Sparse Inverted Index – Haffner and Kanthak
 4. Large-Scale Learning with String Kernels – Sonnenburg, Rätsch and Rieck
 5. Large-Scale Parallel SVM Implementation – Durdanovic, Cosatto and Graf
 6. A Distributed Sequential Solver for Large-Scale SVMs – Yom-Tov
 7. Newton Methods for Fast Semisupervised Linear SVMs – Sindhwani and Keerthi
 8. The Improved Fast Gauss Transform with Applications to Machine Learning – Raykar and Duraiswami
 9. Approximation Methods for Gaussian Process Regression – Quinonero-Candela, Rasmussen and Williams
10. Brisk Kernel Independent Component Analysis – Jegelka and Gretton
11. Building SVMs with Reduced Classifier Complexity – Keerthi, Chapelle and DeCoste
12. Trading Convexity for Scalability – Collobert, Sinz, Weston, and Bottou
13. Training Invariant SVMs Using Selective Sampling – Loosli, Bottou and Canu 
14. Scaling Learning Algorithms toward AI – Bengio and LeCun