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71. Geppert H, Humrich J, Stumpfe D, Gaertner T, Bajorath J (2009) Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint
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73. Cawley GC, Talbot NLC (2007) Preventing over-fitting during model selection via bayesian
regularisation of the hyper-parameters. J Mach Learn Res 8:841–861
74. Hall P, Robinson AP (2009) Reducing variability of crossvalidation for smoothing-parameter
choice. Biometrika 96(1):175–186. doi:10.1093/biomet/asn068
75. Gönen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res
12:2211–2268
76. Tax DMJ, Duin RPW (2004) Support vector data description. Mach Learn 54(1):45–66
77. Smola AJ, Mangasarian OL, Scholkopf B (2002) Sparse kernel feature analysis. In: classification, automation, and new media. Studies in classification, data analysis, and knowledge
organization, pp 167–178
78. Filippone M, Camastra F, Masulli F, Rovetta S (2008) A survey of kernel and spectral methods for clustering. Pattern Recogn 41(1):176–190. doi:10.1016/j.patcog.2007.05.018
79. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664
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Accessed 11 August 2014.
459
62. Hamsici OC, Martinez AM (2009) Rotation invariant kernels and their application to shape
analysis. IEEE Trans Pattern Anal 31(11):1985–1999. doi:10.1109/tpami.2008.234
63. Haasdonk B, Burkhardt H (2007) Invariant kernel functions for pattern analysis and machine
learning. Mach Learn 68(1):35–61. doi:10.1007/s10994-007-5009-7
64. Wood J (1996) Invariant pattern recognition: A review. Pattern Recogn 29(1):1–17.
doi:10.1016/0031-3203(95)00069-0
65. Azencott CA, Ksikes A, Swamidass SJ, Chen JH, Ralaivola L, Baldi P (2007) One- to fourdimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties. J Chem Inf Mod 47(3):965–974
66. Bishop CM (2006) Pattern ecognition and machine learning. Information science and statistics. Springer, New York
67. Baskin II, Zhokhova NI, Palyulin VA, Zefirov NS (2008) Additive inductive learning in
QSAR/QSPR studies and molecular modeling. In: 4th German conference on chemoinformatics, November 9–11, 2008, Goslar, Germany, p 78
68. Erhan D, L’Heureux P-J, Yue SY, Bengio Y (2006) Collaborative filtering on a family of
biological targets. J Chem Inf Model 46(2):626–635
69. Faulon J-L, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolite
and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24(2):225–233. doi:10.1093/bioinformatics/btm580
70. Jacob L, Vert JP (2008) Protein-ligand interaction prediction: an improved chemogenomics
approach. Bioinformatics 24(19):2149–2156
71. Geppert H, Humrich J, Stumpfe D, Gaertner T, Bajorath J (2009) Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint
descriptors. J Chem Inf Mod 49(4):767–779. doi:10.1021/ci900004a
72. Cawley GC, Talbot NLC (2010) On over-fitting in model selection and subsequent selection
bias in performance evaluation. J Mach Learn Res 11:2079–2107
73. Cawley GC, Talbot NLC (2007) Preventing over-fitting during model selection via bayesian
regularisation of the hyper-parameters. J Mach Learn Res 8:841–861
74. Hall P, Robinson AP (2009) Reducing variability of crossvalidation for smoothing-parameter
choice. Biometrika 96(1):175–186. doi:10.1093/biomet/asn068
75. Gönen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res
12:2211–2268
76. Tax DMJ, Duin RPW (2004) Support vector data description. Mach Learn 54(1):45–66
77. Smola AJ, Mangasarian OL, Scholkopf B (2002) Sparse kernel feature analysis. In: classification, automation, and new media. Studies in classification, data analysis, and knowledge
organization, pp 167–178
78. Filippone M, Camastra F, Masulli F, Rovetta S (2008) A survey of kernel and spectral methods for clustering. Pattern Recogn 41(1):176–190. doi:10.1016/j.patcog.2007.05.018
79. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664
80. R: a language and environment for statistical computing. (2012). http://www.R-project.org/.
Accessed 11 August 2014.