Nonlinear Prediction of Quantitative Structure-Activity Relationships
暂无分享,去创建一个
Peter Tiño | Yi Sun | Ian T. Nabney | Bruce S. Williams | Jens Lösel | I. Nabney | P. Tiňo | Yi Sun | B. S. Williams | J. Lösel
[1] A. Leo,et al. Substituent constants for correlation analysis in chemistry and biology , 1979 .
[2] Gene H. Golub,et al. Matrix computations , 1983 .
[3] A. Ghose,et al. Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity , 1986 .
[4] Arup K. Ghose,et al. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics , 1989, J. Chem. Inf. Comput. Sci..
[5] H. White,et al. Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.
[6] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[7] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[8] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[9] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[10] S. Hirono,et al. Simple Method of Calculating Octanol/Water Partition Coefficient. , 1992 .
[11] Martin Fodslette Møller,et al. A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.
[12] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[13] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[14] Luís Torgo,et al. Functional Models for Regression Tree Leaves , 1997, ICML.
[15] J. Sangster. Octanol-Water Partition Coefficients: Fundamentals and Physical Chemistry , 1997 .
[16] Christopher K. I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.
[17] Han van de Waterbeemd,et al. Substructure and whole molecule approaches for calculating log P , 2001, J. Comput. Aided Mol. Des..
[18] Ian T. Nabney,et al. Netlab: Algorithms for Pattern Recognition , 2002 .
[19] Lehel Csató,et al. Sparse On-Line Gaussian Processes , 2002, Neural Computation.
[20] Peter Tiño,et al. Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way , 2002, IEEE Trans. Pattern Anal. Mach. Intell..