Ahlgren P, Jarneving B, Rousseau R. (2003). Requirements for a cocitation similarity measure with special reference to Pearson's correlation coefficient J Am Soc Inf Sci Tech. 54

Bartlett P, Shawe-taylor J, Williamson R, Anthony M. (1998). Structural risk minimization over data-dependent hierarchies IEEE Trans Info Theory. 44

Bayer AE, Smart JC, Mclaughlin GW. (1990). Mapping intellectual structure of a scientific subfield through author cocitations J Am Soc Inf Sci. 41

Benjamini Y, Yekutieli D. (2001). The control of the false discovery rate in multiple testing under dependency Ann Stat. 29

Blum AL, Langley P. (1997). Selection of relevant feature and examples in machine learning Art Intell. 97

Califano A, Stolovitzky G, Tu Y. (1999). Analysis of gene expression microarrays for phenotype classification Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology.

Chu W, Keerthi SS, Ong CJ. (2004). Bayesian support vector regression using a unified loss function. IEEE transactions on neural networks. 15 [PubMed]

Guyon I, Elisseeff A. (2003). An introduction to variable and feature selection J Mach Learn Res. 3

Heyer LJ, Kruglyak S, Yooseph S. (1999). Exploring expression data: identification and analysis of coexpressed genes. Genome research. 9 [PubMed]

Hochberg Y, Benjamini Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing Jr Stat Soc Ser B. 57

Hoff PD. (2005). Bilinear mixed-effects models for dyadic data J Am Stat Assoc. 100

Hofmann T, Buhmann JM. (1997). Pairwise data clustering by deterministic annealing IEEE Transactions On Pattern Analysis And Machine Intelligen. 19

Kleinberg JM. (1999). Authoritative sources in a hyperlinked environment J Assoc Comput Mach. 46

Kohavi R, John GH. (1996). Wrappers for feature subset selection Artificial Intelligence. 97

Lee JK et al. (2000). A gene expression database for the molecular pharmacology of cancer. Nat Genet. 24

Li H, Loken E. (2002). A unified theory of statistical analysis and inference for variance component models for dyadic data Statistica Sinica. 12

Lysov Y et al. (1988). DNA sequencing by hybridization with oligonucleotides Doklady Academy Nauk USSR. 303

Mangasarian OL. (1998). Generalized support vector machines Tech. Rep. No. 98-14.

Muller KR, Ratsch G, Onoda T. (2001). Soft margins for AdaBoost Mach Learn. 42

Obermayer K, Graepel T, Herbrich R. (2000). Large margin rank boundaries for ordinal regression Advances in large margin classifiers.

Obermayer K, Graepel T, Herbrich R, Bollmann-Sdorra P. (1999). Classification on pairwise proximity data Advances in neural information processing systems. 11

Obermayer K, Hochreiter S. (2004). Classification, regression, and feature selection on matrix data Tech. Rep. No. 2004-2.

Obermayer K, Hochreiter S. (2004). Gene selection for microarray data Kernel methods in computational biology.

Obermayer K, Hochreiter S. (2004). Sphered support vector machine Tech Rep.

Obermayer K, Hochreiter S. (2005). Nonlinear feature selection with the potential support vector machine Feature extraction, foundations and applications.

Obermayer K, Mozer MC, Hochreiter S. (2003). Coulomb classifiers: Generalizing support vector machines via an analogy to electrostatic systems Advances in neural information processing systems. 15

Pomeroy SL et al. (2002). Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature. 415 [PubMed]

Puzicha J, Hofmann T. (1998). Unsupervised learning from dyadic data Tech. Rep. No. TR-98-042.

Salton G. (1968). Automatic information organization and retrieval.

Scholkopf B, Muller KR, Mika S, Ratsch G, Weston J. (1999). Fisher discriminant analysis with kernels Neural networks for signal processing IX.

Scholkopf B, Shawe-Taylor J, Smola AJ, Williamson RC. (1999). Generalization bounds via eigenvalues of the gram matrix Tech. Rep. No. NC2-TR-1999-035.

Scholkopf B, Smola AJ. (2001). Learning with kernels: Support vector machines, regularization, optimization, and beyond.

Shipp MA et al. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine. 8 [PubMed]

Vapnik V. (1998). Statistical Learning Theory.

Werner D. (2000). Funktional analysis (3rd ed).

White HD, Mccain KW. (1989). Bibliometrics Ann Rev Inf Sci Tech. 24

Williamson R, Anthony M, Shawe-Taylor J, Bartlett PL. (1996). A framework for structural risk minimization Proceedings Of The 9th Annual Conference On Computational Learning Theory.

Knebel T, Hochreiter S, Obermayer K. (2008). An SMO algorithm for the potential support vector machine. Neural computation. 20 [PubMed]