MIWOCI Workshop 2014

[1]  J. Giles.  Classes of semi-inner-product spaces , 1967 .

[2]  Volker Tresp,et al.  Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model , 1998, NIPS.

[3]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[4]  I. Jolliffe.  Principal Component Analysis , 2002 .

[5]  G. Lumer.  SEMI-INNER-PRODUCT SPACES , 1961 .

[6]  Thomas Villmann,et al.  Non-Euclidean principal component analysis by Hebbian learning , 2015, Neurocomputing.

[7]  Jesse Davis,et al.  Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation , 2012, ICML.

[8]  Atsushi Sato,et al.  Generalized Learning Vector Quantization , 1995, NIPS.

[9]  William H. Press,et al.  Numerical recipes in C , 2002 .

[10]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[11]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[12]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[13]  Thomas Villmann,et al.  Generalized relevance learning vector quantization , 2002, Neural Networks.

[14]  David Page,et al.  Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals , 2013, ECML/PKDD.

[15]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[16]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[17]  L. Tarassenko,et al.  Bayesian Extreme Value Statistics for Novelty Detection in Gas-Turbine Engines , 2008, 2008 IEEE Aerospace Conference.

[18]  Ulf Brefeld,et al.  {AUC} maximizing support vector learning , 2005 .

[19]  Michael Biehl,et al.  Adaptive Relevance Matrices in Learning Vector Quantization , 2009, Neural Computation.

[20]  Erkki Oja,et al.  Nonlinear PCA: Algorithms and Applications , 1993 .

[21]  Thomas Villmann,et al.  Border-Sensitive Learning in Kernelized Learning Vector Quantization , 2013, IWANN.

[22]  Lionel Tarassenko,et al.  A System for the Analysis of Jet Engine Vibration Data , 1999, Integr. Comput. Aided Eng..

[23]  Thomas Villmann,et al.  Kernelized vector quantization in gradient-descent learning , 2015, Neurocomputing.

[24]  E. Hellinger,et al.  Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. , 1909 .

[25]  John A. Quinn,et al.  Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care , 2005, NIPS.

[26]  J. Keilwagen,et al.  Area under Precision-Recall Curves for Weighted and Unweighted Data , 2014, PloS one.

[27]  Guido Sanguinetti,et al.  Information theoretic novelty detection , 2010, Pattern Recognit..

[28]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[29]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

[30]  Robert P. W. Duin,et al.  Precision-recall operating characteristic (P-ROC) curves in imprecise environments , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[31]  H. Altay Güvenir,et al.  Ranking Instances by Maximizing the Area under ROC Curve , 2013, IEEE Transactions on Knowledge and Data Engineering.

[32]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[33]  William Nick Street,et al.  Learning to Rank by Maximizing AUC with Linear Programming , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[34]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .