Literature

Signature	Author(s)	year	Title
Agrawal/Srikant/94a	R. Agrawal and R. Srikant	1994	Fast Algorithms for Mining Association Rules in Large Data Bases
Aha/97a		1997	Lazy Learning
Aha/etal/91a	Aha, David and Kibler, Dennis and Albert, Marc	1991	Instance-based learning algorithms
Bennett/99a	K. Bennett	1999	Combining Support Vector and Mathematical Programming Methods for Classification
Bernstein/etal/2002a	Abraham Bernstein and Shawndra Hill and Foster Provost	2002	An Intelligent Assistant for the Knowledge Discovery Process
Brazdil/98a	Pavel Brazdil	1998	Data Transformation and Model Selection by Experimentation and Meta-Learning
Breiman/etal/84b	Breiman, Leo and Friedman, Jerome H. and Olshen, Richard A. and Stone, Charles J.	1984	Classification and Regression Trees
Burges/98a	C. Burges	1998	A Tutorial on Support Vector Machines for Pattern Recognition
Carbonara/Sleeman/99a	Carbonara, L. and Sleeman, Derek H.	1999	Effective and Efficient Knowledge Base Refinement
Craw/Sleeman/95a	Craw, S. and Sleeman, Derek H.	1995	Automating the refinement of knowledge-based systems
Draper/Smith/73a	Draper, N. and Smith, H.	1973	Applied Regression Analysis
Engels/96a	Engels, Robert	1996	Planning Tasks for Knowledge Discovery in Databases; Performing Task--Oriented User--Guidance
Engels/etal/97a	Robert Engels and Guido Lindner and Rudi Studer	1997	A Guided Tour through the Data Mining Jungle
ESS/95a		1995	Encyclopedia of Statistical Science
Fan/95a	Fan, J.	1995	Local Modelling
Fisher/87c	Douglas H. Fisher	1987	Knowledge Acquisition Via Incremental Conceptual Clustering
Friedman/91a	Friedman, Jerome H.	1991	Multivariate Adaptive Regression Splines
Friedman/Stuetzle/81a	Friedman, Jerome H. and Stuetzle, W.	1981	Projection pursuit regression
Gaerdenfors/88a	G?rdenfors, Peter	1988	Knowledge in Flux --- Modeling the Dynamics of Epistemic States
Ganascia/90a	Ganascia, Jean-Gabriel	1990	L'^ame-machine
Ginsberg/88b	Ginsberg, A.	1988	Automatic Refinement of expert system knowledge bases
Goerz/etal/2000a	Görz, G. and Schneeberger, J. and Rollinger, C.	2000	Handbuch der künstlichen Intelligenz
Hastie/Tibshirani/90a	Hastie, T. and Tibshirani, R.	1990	Generalized Additive Models
ICML/99a		1999	International Conference on Machine Learning
Joachims/98a	Joachims, Thorsten	1998	Text Categorization with Support Vector Machines: Learning with Many Relevant Features
Joachims/99a	Joachims, Thorsten	1999	Making large-Scale SVM Learning Practical
Joachims/99c	Thorsten Joachims	1999	Transductive Inference for Text Classification using Support Vector Machines
Karalic/95a	Karalic, A.	1995	First Order Regression
Karalic/Bratko/97a	Karalic, A. and Bratko, I.	1997	First Order Regression
Kodratoff/Michalski/90a		1990	Machine Learning --- An Artificial Intelligence Approach
Loader/Cleveland/95a	Loader, C. and Cleveland, W.	1995	Computational Statistics
Loader/Cleveland/95b	Loader, C. and Cleveland, W.	1995	Smoothing by Local Regression: Principles and Methods (with discussion)
Mahoney/Mooney/93a	Mahoney, J. J. and Mooney, R. J.	1993	Combining connectionist and symbolic learning to refine certainty-factor rule-bases
Mitchell/97b	Mitchell, Tom M.	1997	Machine Learning
Mueller/etal/99a		1999	Neural Information Processing Systems
Muggleton/95a	Stephen Muggleton	1995	Inverting Entailment and Progol
Nadaraya/64a	Nadaraya, E. A.	1964	On estimating regression
Ohsuga/etal/2001a	Ohsuga, S. and Zhong, N. and Liu, C.	2001	Dynamically Organizing KDD Processes.
Ourston/Mooney/90a	Ourston, D. and Mooney, R. J.	1990	Changing the rules: A comprehensive approach to theory refinement
Parzen/62a	Parzen, E.	1962	On estimation of a probability density function and mode
Press/92a	Press, W. H.	1992	Numerical Recipes in C
Quinlan/90a	Quinlan, J.R.	1990	Learning Logical Definitions from Relations
Quinlan/92a	Quinlan, J. Ross	1992	Learning with Continuous Classes
Quinlan/93b	John Ross Quinlan	1993	C4.5: Programs for Machine Learning
Quinlan/93c	Quinlan, J. Ross	1993	Combining Instance-based and Model-based Learning
Quinlan/96a	J.R. Quinlan	1996	Learning First-Order Definitions of Functions
Ras/Skowron/97a		1997	Foundations of Intelligent Systems
Richards/Mooney/95a	Richards, B. J. and Mooney, R. J.	1995	Automated refinement of first-order Horn-clause domain theories
Rissanen/82a	J. Rissanen	1982	A universal prior for integers and estimation by the minimum description length
Rosenblatt/56a	Rosenblatt, M.	1956	Remarks on some nonparametric estimates of a density function
Schoelkopf/etal/2000a	Sch\"olkopf, Bernhard and Smola, Alex J. and Williamson, Robert C. and Bartlett, Peter L.	2000	New Support Vector Algorithms
Shapire/99a	Robert E. Shapire	1999	Theoretical Views of Boosting and Applications
Shavlik/etal/90a	Shavlik, J. W. and Noordewier, N. O. and Towell, G. G.	1990	Refinement of approximate domain theories by knowledge-based neural networks
Torgo/95a	Torgo, L.	1995	Data Fitting with Rule-based Regression
Torgo/Gama/96a	Torgo, L. and Gama, J.	1996	Regression by Classification
Utgoff/89a	Utgoff, P. E.	1989	Incremental Induction of Decision Trees
Vapnik/95a	Vladimir N. Vapnik	1995	The Nature of Statistical Learning Theory
Vapnik/98a	V. Vapnik	1998	Statistical Learning Theory
Velde/89a	Van de Velde, Walter	1989	IDL: Taming the Multiplexer
Watson/64a	Watson, G. S.	1964	Smooth Regression Analysis
Weiss/Indurkhya/93a	Sholom M. Weiss and Nitin Indurkhya	1993	Rule--Based Regression
Weiss/Indurkhya/95a	Weiss, S. and Indurkhya, N.	1995	Rule-based Machine Learning Methods for Functional Prediction
Wilkins/90a	Wilkins, David C.	1990	Knowledge Base Refinement as Improving an Incorrect and Incomplete Domain Theory
Wogulis/Pazzani/93a	James Wogulis and Michael J. Pazzani	1993	A Methodology for Evaluating Theory Revision Systems: Results with Audrey II
Wrobel/94b	Stefan Wrobel	1994	Concept Formation During Interactive Theory Revision
Wrobel/etal/2000a	Wrobel, S. and Morik, K. and Joachims, T.	2000	Maschinelles Lernen und Data Mining
Zhang/99a	Wei Zhang	1999	A Region-Based Approach to Discovering Temporal Structures in Data
Zhong/etal/97a	N. Zhong and C. Liu and S. Ohsuga	1997	A Way of Increasing both Autonomy and Versatility of a KDD System