Support Vector Machines - and other kernel-based learning methods
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Support Vector Machines and other kernel-based learning methods
John Shawe-Taylor & Nello Cristianini - Cambridge University Press, 2000 - Ordering Info »

Nello Cristianini

All My Publications

Books:

An Introduction to Support Vector Machines
(Nello Cristianini and John Shawe-Taylor);
Cambridge University Press; 2000
(www.support-vector.net)
(+Chinese Edition, 2004)
(+ Japanese Edition, 2005)

Kernel Methods for Pattern Analysis
(John Shawe-Taylor and Nello Cristianini);
Cambridge University Press; 2004
(www.kernel-methods.net)
(+ Chinese Edition, 2005)

Introduction to Computational Genomics
(Nello Cristianini and Matthew Hahn)
Cambridge University Press; 2006
(www.computational-genomics.net)

Papers (by year):

2007

A Kernel Canonical Correlation Analysis for Learning the Semantics of Text,
B. Fortuna, N. Cristianini, and J. Shawe-Taylor,
Kernel Methods in Bioengineering, Signal and Image Processing,
edited by Dr. Gustavo Camps-Valls, Dr. Jose Luis Rojo-Alvarez, and Dr. Manel Martinez-Ramon [to appear]
Kernel Methods,
N. Cristianini, J. Shawe-Taylor, and C. Saunders,Kernel Methods in Bioengineering, Signal and Image Processing,
edited by Dr. Gustavo Camps-Valls, Dr. Jose Luis Rojo-Alvarez, and Dr. Manel Martinez-Ramon, [to appear]

 

2006

On Kernel Target Alignment
Nello Cristianini, Jaz Kandola, Andre Elisseeff and John Shawe-Taylor
In "Innovations in Machine Learning: Theory and Application"s
Editors: Dawn Holmes, Lakhmi Jain; ISBN: 3-540-30609-9
Springer Verlag, 2006 [PS][older version]

The Evolution of Mammalian Gene Families
(Demuth JP, Bie TD, Stajich JE, Cristianini N, Hahn MW.)
PLoS ONE. 2006 Dec 20;1(1):e85.
Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems
Tijl De Bie and Nello Cristianini,
Journal of Machine Learning Research, 7(Jul):1409--1436, 2006.
CAFE: a computational tool for the study of gene family evolution
Tijl De Bie, Nello Cristianini, Jeffery P. Demuth, and Matthew W. H
Bioinformatics 2006 22(10):1269-1271; doi:10.1093/bioinformatics/btl097
Modeling Sequence Evolution with Kernel Methods
M. Bresco, M. Turchi, T. De Bie, N. Cristianini
Computational Optimization and Applications – In Press

 

2005

On the Eigenspectrum of the Gram matrix and the generalization error of kernel PCA (John Shawe-Taylor, Chris Williams, Nello Cristianini, Jaz S. Kandola) IEEE Tranactions on Information Theory 51(7) 2510-2522 (2005)

Estimating the tempo and mode of gene family evolution from comparative genomic data
M. Hahn, T. de Bie, C. Nguyen, J. Stajich, N. Cristianini
Genome Research 15:1153-1160, 2005

Eigenproblems in Pattern Recognition, (De Bie T., Cristianini N., Rosipal R.,) “Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics”, E. Bayro-Corrochano (editor), Springer-Verlag, 2005

Discovering regulatory modules from heterogeneous information sources, De Bie T., Monsieurs P., Engelen K., De Moor B., Cristianini N., Marchal K., To Appear: Proceedings of the Pacific Symposium on Biocomputing (PSB). , 2005

 

2004

Kernel-based Integration of Genomic Data using Semidefinite Programming . (Lanckriet, G.R.G., Cristianini, N., Jordan, M.I., Noble, W.S.) In B. Schoelkopf, K. Tsuda and J.-P. Vert (Eds.), Kernel Methods in Computational Biology: MIT Press. (2004)

A Statistical Framework for Genomic Data Fusion, (Lanckriet G., De Bie T., Cristianini N., Jordan M., Stafford Noble W.,) Bioinformatics (to appear: advance access published on May 6, 2004, DOI 10.1093/bioinformatics/bth294). (2004)
Learning the Kernel Matrix with Semidefinite Programming. (Lanckriet, G.R.G., Cristianini, N., Bartlett, P., El Ghaoui, L., Jordan, M.I.) Journal of Machine Learning Research, 5, 27-72, 2004. (2004).

Kernel-based Data Fusion and its Application to Protein Function Prediction in Yeast . (Lanckriet, G.R.G., Deng, M., Cristianini, N. , Jordan, M.I., Noble, W.S.) Proceedings of the Pacific Symposium on Biocomputing (PSB). , 2004

Kernel methods for exploratory data analysis: a demonstration on text data, (De Bie T., Cristianini N.,) in: Structural, Syntactic, and Statistical Pattern Recognition, Proc. Joint IAPR International Workshops SSPR 2004 and SPR 2004 (Lisbon, Portugal, August 18-20, 2004), Lecture Notes in Computer Science, 3139, Springer Verlag, Berlin, 2004.

 

2003

Convex Methods for Transduction, (De Bie T., Cristianini N.,) Neural Information Processing Systems (NIPS2003), Vancouver, Canada, December 2003.

Efficiently Learning the Metric using Side-Information, (De Bie T., Momma M., Cristianini N.,) in Proc. of the 14th International Conference on Algorithmic Learning Theory (ALT2003), Sapporo, Japan, Lecture Notes in Artificial Intelligence, Vol. 2842, pp. 175-189, Springer, 2003.

 

2002

Kernel Methods: Current Research and Future Directions Nello Cristianini, Colin Campbell, Chris Burges, Machine Learning 46(1/3): 5-9, January 2002
Support Vector Machines and Kernel Methods, The New Generation of Learning Machines (Nello Cristianini, Bernhard Schoelkopf)
Artificial Intelligence Magazine, Volume 23, No 3 pg 31-41

Latent Semantic Kernels (Nello Cristianini, Huma Lodhi, John Shawe-Taylor) Journal of Intelligent Information Systems (JJIS) Vol. 18, No. 2 (March 2002) [PS]


String Matching Kernels for Text Classification, (Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins) Journal of Machine Learning Research, 2(Feb):419-444, 2002 [PS]


On the Generalisation of Soft Margin Algorithms (Shawe-Taylor, J. and Cristianini, N., IEEE Transactions on Information Theory 48(10):pp. 2721-2735. (2002) [PS]

On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum.
(John Shawe-Taylor, Chris Williams, Nello Cristianini, Jaz S. Kandola) -- ALT 2002: 23-40 [PDF]

Learning Semantic Similarity
(Jaz Kandola, Nello Cristianini, John Shawe-Taylor)
NIPS 2002 [PS]

Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis
(Alexei Vinokourov, Nello Cristianini, John Shawe-Taylor)
NIPS 2002 [PDF]

Learning The Kernel Matrix with Semi-Definite Programming
(Gert Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Gahoui, Michael Jordan)
ICML 2002 [PDF]

 

2001

Discrete Kernels for Text Categorisation (Huma Lodhi, John Shawe-Taylor, Nello Cristianini, Chris Watkins) (In Advances in Neural Information Processing Systems (NIPS), vol. 13. (2001) [PS]

On Kernel-Target Alignment,
(Nello Cristianini, John Shawe-Taylor, Andre Elisseeff, Jaz Kandola), NIPS 2001 [PS]

Spectral Kernel Methods for Clustering
(Nello Cristianini, John Shawe-Taylor, Jaz Kandola)
NIPS 2001 [PS]

On the Concentration of Spectral Properties
(John Shawe-Taylor, Nello Cristianini, Jaz Kandola) NIPS 2001 [PS]

Latent Semantic Kernels
(Nello Cristianini, Huma Lodhi, John Shawe-Taylor)
ICML2001

Combination Kernels for Hypertext
(Thorsten Joachims, Nello Cristianini and John Shawe-Taylor),
ICML2001 [PDF]

 

2000

Enlarging the Margin in Perceptron Decision Trees; (Kristin Bennett; Nello Cristianini; John Shawe-Taylor; Donghui Wu; ); Machine Learning 41(3): 295-313, December 2000 [PDF]


Support Vector Machine Classification of Microarray Gene Expression Data (Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Sugnet, Manuel Ares, Jr., David Haussler); PNAS -.Proc. Natl. Acad. Sci. USA, vol. 97, pages 262-267 (2000) [PDF]


Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data (Terry Furey, Nello Cristianini, Nigel Duffy, Michel Schummer, David Bednarski, David Haussler) Bioinformatics, 16(10): 906-914 (2000) [PDF]

Large Margin DAGs for Multiclass Classification;
(John Platt; Nello Cristianini; John Shawe-Taylor).
In Advances in Neural Information Processing Systems (NIPS), vol. 12. (2000) [PS]

Query Strategies for Large Margin Cassifiers
(Colin Campbell, Nello Cristianini, Alex Smola)
In Proceedings of the Seventeenth International Conference on Machine Learning, (ICML 2000) [PS]

 

1999

Large Margin Classifiers and Bayesian Voting Schemes (Nello Cristianini and John Shawe-Taylor ) in: Advances in Kernel Methods - Support Vector Learning}, 1999, MIT Press (Chapter 5, pg.55-68); ed. by B. Schoelkopf, C. Burges, A. Smola [PS]


Soft Margin and Margin Distribution; (John Shawe-Taylor and Nello Cristianini); in: Advances in Large Margin Classifiers; ed. by A. Smola; B. Schoelkopf; P. Bartlett; D. Schuurmans. MIT Press – 1999 [PS]

Dynamically Adapting Kernels in Support Vector Machines;
(Nello Cristianini; Colin Campbell; John Shawe-Taylor)
in: Kearns M., Solla S., Cohn D., editors; Advances in Neural Information Processing Systems (NIPS) vol. 11, 1999, MIT Press [PS]

Multiplicative Updatings for Support Vector Learning
(Nello Cristianini; Colin Campbell; John Shawe-Taylor)
in: Proceedings of: European Symposium on Artificial Neural Networks (ESANN) 1999

Margin Distribution Bounds on Generalization
(John Shawe-Taylor; Nello Cristianini)
in: Proceedings of European Conference on Computational learning Theory, (EuroColt) 1999 [PS]

Large Margin Decision Trees for Induction and Transduction;
(Donghui Wu, Kristin Bennett; Nello Cristianini; John Shawe-Taylor;)
In Proceedings of the Sixteenth International Conference on Machine Learning, (ICML)1999

Further Results on the Margin Distribution;
(John Shawe-Taylor; Nello Cristianini);
In Proceedings of Conference on Computational Learning Theory (COLT), 1999 [PS]

Controlling the Sensitivity of Support Vector Machines;
(Kostas Veropoulos; Colin Campbell; Nello Cristianini);
proceeding of SVM workshop at IJCAI 1999 [PS]

Diagnosis of TBC with Support Vector Machines
(Kostas Veropoulos, Nello Cristianini, Colin Campbell)
in Proceedings of ACAI '99 (Chania, Crete 1999)

 

1998

Large Margin Classification Using the Kernel Adatron Algorithm
(Colin Campbell; Thilo Friess; Nello Cristianini);
In Proceedings of: IDEAL 98

Data Dependent Structural Risk Minimization for Perceptron Decision Trees
(John Shawe-Taylor; Nello Cristianini)
in: Jordan M., Kearns M., Solla S., editors; Advances in Neural Information Processing Systems (NIPS) vol. 10, 1998, MIT Press, pg.336-342 [PS]

Bayesian Classifiers are Large Margin Hyperplanes in a Hilbert Space;
(Nello Cristianini; John Shawe-Taylor; Peter Sykacek)
in: Shavlik, J. (ed) Proceeding of the Fifteenth International Conference on Machine Learning (ICML), 1998,
pg.109-117, and (extended version) submitted to Machine Learning Journal [PS]

The Kernel-Adatron: a Fast and Simple Learning Procedure for Support Vector Machines
(Thilo Friess; Nello Cristianini; Colin Campbell;)
in: Shavlik, J. (ed) Proceeding of the Fifteenth International Conference on Machine Learning (ICML),1998, pg. 188-196