Dear all

After getting more answers to the following question, I repost an improved
summary.

"I am looking for distributed or parallel algorithms for Machine Learning.
I am specially interested on surveys (apart from that by Peter Stone), and
on parallel / distributed versions of stardard algorithms (from decision
trees, bayesian approaches or rulte learners to neural networks, SVMs, etc)."

Thank you to all who replied to my messages, in particular: Hillol
Kargupta, Alex Freitas, Rui Camacho, Balazs Kegl, Claudia Antunes, Jiri
Ocenasek, Jose Carlos Cortizo, Nitesh Chawla and Hillol Kargupta. They have
suggested me the following references:

BOOKS ----------------------------------------------------- *** ADDITIONS

Yike Guo and Robert Grossman, editors, High Performance Data Mining:
Scaling Algorithms, Applications and Systems, Kluwer Academic Publishers, 1999.

A.A. Freitas and S.H. Lavington. Mining Very Large Databases with Parallel
Processing. Kluwer, 1998.
http://www.cs.kent.ac.uk/people/staff/aaf/book-springer-ukc.html

Mohammed J. Zaki, Ching-Tien Ho (Eds). Large-Scale Parallel Data Mining.
Springer-Verlag GmbH, Lecture Notes in Computer Science, 1759 / 2000.

Advances in Distributed and Parallel Knowledge Discovery. Edited by Hillol
Kargupta and Philip Chan
MIT/AAAI Press
http://www.aaai.org/Press/Books/Kargupta1/kargupta1.html

Data Mining: Next Generation Challenges and Future Directions. Edited by H.
Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha
MIT/AAAI Press
http://www.cs.umbc.edu/~hillol/Kargupta/ngdmbook.html
( *This book has several chapters on DDM* )


PAPERS ---------------------------------------------------- *** ADDITIONS

A.A. Freitas. A Survey of Parallel Data Mining. Proc. 2nd Int. Conf. on the
Practical Applications of Knowledge Discovery and Data Mining, 287-300.
London: The Practical Application Company, Mar. 1998.
http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html

Nuno Fonseca, Fernando Silva, Rui Camacho. Strategies to parallelize ILP
systems. In Proceedings of ILC 2005
http://ilp2005.in.tum.de/accepted-papers.html
* Thank you for sending me the draft

S. Gambs, B. Kégl, and E. Aïmeur. "Privacy-preserving boosting" Data Mining
and Knowledge Discovery, 2005 (submitted).
http://www.iro.umontreal.ca/~kegl/research/publications/
* This is for adaboost, and the authors suggest looking at the references:
[Lazarevic and Obradovic, 2002] and [Fan et al., 1999] for adaboost,
[Lindell and Pinkas, 2002] for trees.

Mohammed J. Zaki, "Parallel and Distributed Association Mining: A Survey",
IEEE Concurrency, special issue on Parallel Mechanisms for Data Mining,
Vol. 7, No. 4, pp14-25, December, 1999
* Good intro in terms of pattern mining / association rules

Ocenasek, J., Schwarz, J., Pelikan, M.: Design of Multithreaded Estimation
of Distribution Algorithms. In: Cantú-Paz et al. (Eds.): Genetic and
Evolutionary Computation Conference - GECCO 2003. Springer Verlag: Berlin,
2003, pp. 1247-1258.
* More papers, thesis and code by Jiri Ocenasek (http://jiri.ocenasek.com/).

"Learning Ensembles from Bites: A Scalable and Accurate Approach," Nitesh
V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Journal
of Machine Learning Research (JMLR), 5(Apr):421--451, 2004.

“Distributed Learning with Bagging like Performance,” Chawla, N.V., Moore,
T.E., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P., Springer C. Pattern
Recognition Letters, 24 (1-3) (2003), 455 -- 471.


BIBLIOGRAPHIES -----------------------------------------

Online distributed data mining bibliography:
http://www.cs.umbc.edu/~hillol/DDMBIB/

Again, thank you all. I will imr`pove the summary if I get more replies.

Best regards


Jose Maria Gomez Hidalgo
Departamento de Sistemas Informáticos
Universidad Europea de Madrid
28670 - Villaviciosa de Odon - MADRID
(+34) 912115670
jmgomez@...
http://www.esp.uem.es/~jmgomez/
http://www.esp.uem.es

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