References
- [AU99a]
-
U. Alon, N. Barkai, D. A. Notterman, K. Gish, S.Ybarra, D.Mack, and A. J.
Levine.
Broad patterns of gene expression revealed by clustering analysis of
tumor and normal colon tissues probed by oligonucleotide arrays.
Cell Biology, 96:6745-6750, 1999.
- [MB04a]
-
Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown.
Learning multi-label scene classification.
Pattern Recognition, 37(9):1757-1771, 2004.
- [RC02a]
-
R. Collobert, S. Bengio, and Y. Bengio.
A parallel mixture of SVMs for very large scale problems.
Neural Computation, 14(05):1105-1114, 2002.
- [Chang01d]
-
Chih-Chung Chang and Chih-Jen Lin.
IJCNN 2001 challenge: Generalization ability and text decoding.
In Proceedings of IJCNN. IEEE, 2001.
- [MD04a]
-
M. Duarte and Y. H. Hu.
Vehicle classification in distributed sensor networks.
Journal of Parallel and Distributed Computing, 64(7):826-838,
July 2004.
- [KD02a]
-
K. Duan, S. S. Keerthi, and A. N. Poo.
Evaluation of simple performance measures for tuning SVM
hyperparameters.
Neurocomputing, 51:41-59, 2003.
- [AE02a]
-
André Elisseeff and Jason Weston.
A kernel method for multi-labelled classification.
In Thomas G. Dietterich, Susan Becker, and Zoubin Ghahramani,
editors, Advances in Neural Information Processing Systems 14, 2002.
- [GWF01a]
-
Gary William Flake and Steve Lawrence.
Efficient SVM regression training with SMO.
Machine Learning, 46:271-290, 2002.
- [TG99a]
-
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov,
H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and
E. S. Lander.
Molecular classification of cancer: class discovery and class
prediction by gene expression monitoring.
Science, 286(5439):531, 1999.
- [JLG03a]
-
Jennifer L. Gardy, Cory Spencer, Ke Wang, Martin Ester, Gabor E. Tusnady,
Istvan Simon, Sujun Hua, Katalin deFays, Christophe Lambert, Kenta Nakai, and
Fiona S.L. Brinkman.
PSORT-B: improving protein subcellular localization prediction for
gram-negative bacteria.
Nucleic Acids Research, 31(13):3613-3617, 2003.
- [CWH03a]
-
Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin.
A practical guide to support vector classification.
Technical report, Department of Computer Science, National Taiwan
University, 2003.
- [TKH96a]
-
Tin Kam Ho and Eugene M. Kleinberg.
Building projectable classifiers of arbitrary complexity.
In Proceedings of the 13th International Conference on
Pattern Recognition, pages 880-885, Vienna, Austria, August 1996.
- [CWH01a]
-
Chih-Wei Hsu and Chih-Jen Lin.
A comparison of methods for multi-class support vector machines.
IEEE Transactions on Neural Networks, 13(2):415-425, 2002.
- [JJH94a]
-
J. J. Hull.
A database for handwritten text recognition research.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
16(5):550-554, May 1994.
- [SSK05a]
-
S. Sathiya Keerthi and Dennis DeCoste.
A modified finite Newton method for fast solution of large scale
linear SVMs.
Journal of Machine Learning Research, 6:341-361, 2005.
- [KL95a]
-
Ken Lang.
Newsweeder: Learning to filter netnews.
In Proceedings of the Twelfth International Conference on
Machine Learning, pages 331-339, 1995.
- [YL98a]
-
Yann LeCun, L. Bottou, Y. Bengio, and P. Haffner.
Gradient-based learning applied to document recognition.
Proceedings of the IEEE, 86(11):2278-2324, November 1998.
MNIST database available at http://yann.lecun.com/exdb/mnist/.
- [KML02a]
-
Kuan-Min Lin and Chih-Jen Lin.
A study on reduced support vector machines.
IEEE Transactions on Neural Networks, 14(6):1449-1559, 2003.
- [DL04b]
-
David D. Lewis, Yiming Yang, Tony G. Rose, and Fan Li.
RCV1: A new benchmark collection for text categorization research.
Journal of Machine Learning Research, 5:361-397, 2004.
- [AM98a]
-
Andrew McCallum and Kamal Nigam.
A comparison of event models for naive bayes text classification.
In Proceedings of the AAAI'98 Workshop on Learning for Text
categorization, 1998.
- [JP98a]
-
John C. Platt.
Fast training of support vector machines using sequential minimal
optimization.
In Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J.
Smola, editors, Advances in Kernel Methods - Support Vector Learning,
Cambridge, MA, 1998. MIT Press.
- [DP01a]
-
Danil Prokhorov.
IJCNN 2001 neural network competition.
Slide presentation in IJCNN'01, Ford Research Laboratory, 2001.
http://www.geocities.com/ijcnn/nnc
_ijcnn01.pdf .
- [JR01a]
-
Jason D. M. Rennie.
Improving multi-class text classification with naive bayes.
Master's thesis, Massachusetts Institute of Technology, 2001.
- [JR01b]
-
Jason D. M. Rennie and Ryan Rifkin.
Improving multiclass text classification with the Support Vector
Machine.
Technical Report AIM-2001-026, Massachusetts Insititute of
Technology, 2001.
- [SKS03a]
-
S. K. Shevade and S. S. Keerthi.
A simple and efficient algorithm for gene selection using sparse
logistic regression.
Bioinformatics, 19(17):2246-2253, 2003.
- [JYW02a]
-
Jung-Ying Wang.
Application of support vector machines in bioinformatics.
Master's thesis, Department of Computer Science and Information
Engineering, National Taiwan University, 2002.
- [MW01a]
-
M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H. Zuzan,
J. A. Olson, Jr., J. R. Marks, and J. R. Nevins.
Predicting the clinical status of human breast cancer by using gene
expression profiles.
Proceedings of the National Academy of Sciences,
98:11462-11467, 2001.