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After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.
The Use of Multiple Measurements in Taxonomic Problems
R.A. Fisher · 1936
Earlier work this paper cites.
Carcinogens as Frameshift Mutagens: Metabolites and Derivatives of 2-Acetylaminofluorene and Other Aromatic Amine Carcinogens
Bruce N. Ames, E. G. Gurney, James A. Miller, and H. Bartsch · 1972
Earlier work this paper cites.
Bacterial mutagenicity investigation of epoxides: drugs, drug metabolites, steroids and pesticides
Hansruedi Glatt, Reinhard Jung, and Franz Oesch · 1983
Earlier work this paper cites.
Robust Statistics: The Approach Based on Influence Functions
F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel · 1986
Earlier work this paper cites.
Decision theory in expert systems and artificial interigence
E. J. Horvitz, J. S. Breese, and M. Henrion · 1988
Earlier work this paper cites.
Explanation in Bayesian Belief Networks
H. Suermondt · 1992
Earlier work this paper cites.
Neural Networks for Pattern Recognition
C.M. Bishop · 1995
Earlier work this paper cites.
The Nature of Statistical Learning Theory
V. Vapnik · 1995
Earlier work this paper cites.
A Probabilistic Theory of Pattern Recognition
L. Devroye, L. Györfi, and G. Lugosi · 1996
Earlier work this paper cites.
Efficient backprop
Y. LeCun, L. Bottou, G.B. Orr, and K.-R. Müller · 1998
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John C. Platt · 1999
Earlier work this paper cites.
Symmetrizing the Kullback-Leibler distance
Don H. Johnson and Sinan Sinanovic · 2000
Earlier work this paper cites.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
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An introduction to kernel-based learning algorithms
K.R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf · 2001
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A methodology to explain neural network classification
Raphael Féraud and Fabrice Clérot · 2002
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B. Schölkopf and A. Smola · 2002
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SVM Toolbox for Matlab, Jan 2002
A. Schwaighofer · 2002
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Isabelle Guyon and André Elisseeff · 2003
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Accurate solubility prediction with error bars for electrolytes: A machine learning approach
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak, Detlev Sülzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller · 2007
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Automatic QSAR modeling of adme properties: blood-brain barrier penetration and aqueous solubility
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Explaining classifications for individual instances
Marko Robnik-Šikonja and Igor Kononenko · 2008
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A probabilistic approach to classifying metabolic stability
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Katja Hansen, Antonius ter Laak, Philip Lienau, Andreas Reichel, Nikolaus Heinrich, and Klaus-Robert Müller · 2008
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Jeroen Kazius, Ross McGuire, and Roberta Bursi · 2005
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Assesing approximate inference for bianry gaussian process classification
M. Kuss and C. E. Ramussen · 2005
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
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Dragon for windows and linux 2006
R. Todeschini, V. Consonni, A. Mauri, and M. Pavan · 2006
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Estimating the domain of applicability for machine learning QSAR models: A study on aqueous solubility of drug discovery molecules
Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich, and Klaus-Robert Müller
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POIMs: positional oligomer importance matrices — understanding support vector machine based signal detectors
Sören Sonnenburg, Alexander Zien, Petra Philips, and Gunnar Rätsch · 2008
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Towards a model independent method for explaining classification for individual instances
Erik Štrumbelj and Igor Kononenko · 2008
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A benchmark data set for in silico prediction of ames mutagenicity
Katja Hansen, Sebastian Mika, Timon Schroeter, Andreas Sutter, Antonius Ter Laak, Thomas Steger-Hartmann, Nikolaus Heinrich, and Klaus-Robert Müller · 2009
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Center-surround patterns emerge as optimal predictors for human saccade targets
W. Kienzle, M. O. Franz, B. Schölkopf, and F. A. Wichmann · 2009
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Dataset Shift in Machine Learning
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Finding stationary subspaces in multivariate time series
Paul von Bünau, Frank C Meinecke, Franz J Király, and Klaus-Robert Müller · 2009
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