Fritz Wysotzki - researchr alias

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Viewing Publication 1 - 57 from 57

2009

A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree LearningFritz Wysotzki, Peter Geibel. advai, 2009, 2009. [doi]

2005

2004

Applied Connectionistic Methods to compare Segmented ImagesStefan Bischoff, Fritz Wysotzki. ki, 18(1):11, 2004. [doi]

SVM learning with the SH inner productPeter Geibel, Brijnesh J. Jain, Fritz Wysotzki. esann 2004: 299-304 [doi]

Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent CostsPeter Geibel, Fritz Wysotzki. apin, 21(1):45-56, 2004. [doi]

Perceptron and SVM learning with generalized cost modelsPeter Geibel, Ulf Brefeld, Fritz Wysotzki. ida, 8(5):439-455, 2004. [doi]

Spatial Inference - Application of Machine Learning AlgorithmsCarsten Gips, Fritz Wysotzki. lwa 2004: 155-160

Learning with Neural Networks in the Domain of GraphsBrijnesh J. Jain, Fritz Wysotzki. lwa 2004: 163-170

Discrimination networks for maximum selectionBrijnesh J. Jain, Fritz Wysotzki. NN, 17(1):143-154, 2004. [doi]

Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern RecognitionBrijnesh J. Jain, Peter Geibel, Fritz Wysotzki. ki 2004: 241-255 [doi]

The maximum weighted clique problem and Hopfield networksBrijnesh J. Jain, Fritz Wysotzki. esann 2004: 331-336 [doi]

Central Clustering of Attributed GraphsBrijnesh J. Jain, Fritz Wysotzki. ml, 56(1-3):169-207, 2004. [doi]

Structural Perceptrons for Attributed GraphsBrijnesh J. Jain, Fritz Wysotzki. gacm 2004: 85-94 [doi]

2003

Applied Connectionistic Methods in Computer Vision to Compare Segmented ImagesStefan Bischoff, D. Reuss, Fritz Wysotzki. ki 2003: 312-326 [doi]

Support Vector Machines with Example Dependent CostsUlf Brefeld, Peter Geibel, Fritz Wysotzki. ecml 2003: 23-34 [doi]

Connectionist construction of prototypes from decision trees for graph classificationPeter Geibel, Kristina Schädler, Fritz Wysotzki. ida, 7(2):125-140, 2003. [doi]

Perceptron Based Learning with Example Dependent and Noisy CostsPeter Geibel, Fritz Wysotzki. icml 2003: 218-225

Learning Linear Classifiers Sensitive to Example Dependent and Noisy CostsPeter Geibel, Ulf Brefeld, Fritz Wysotzki. ida 2003: 167-178 [doi]

Spatial Inference - Combining Learning and Constraint SolvingCarsten Gips, Fritz Wysotzki. ki 2003: 282-296 [doi]

A Neural Graph Isomorphism Algorithm based on local InvariantsBrijnesh J. Jain, Fritz Wysotzki. esann 2003: 79-84 [doi]

A Novel Neural Network Approach to Solve Exact and Inexact Graph Isomorphism ProblemsBrijnesh J. Jain, Fritz Wysotzki. icann 2003: 299-306 [doi]

Automorphism Partitioning with Neural NetworksBrijnesh J. Jain, Fritz Wysotzki. npl, 17(2):205-215, 2003.

A k-Winner-Takes-All Classifier for Structured DataBrijnesh J. Jain, Fritz Wysotzki. ki 2003: 342-354 [doi]

An Associative Memory for the Automorphism Group of StructuresBrijnesh J. Jain, Fritz Wysotzki. esann 2003: 107-112 [doi]

A Competitive Winner-Takes-All Architecture for Classification and Pattern Recognition of StructuresBrijnesh J. Jain, Fritz Wysotzki. gbrpr 2003: 259-270 [doi]

2002

2001

On the short-term-memory of WTA netsBrijnesh J. Jain, Fritz Wysotzki. esann 2001: 289-294 [doi]

Efficient Pattern Discrimination with Inhibitory WTA NetsBrijnesh J. Jain, Fritz Wysotzki. icann 2001: 827-834 [doi]

2000

Graphbasierte Lernverfahren für relationale DatenPeter Geibel, Fritz Wysotzki. ife, 15(1):1-15, 2000. [doi]

Applying Inductive Program Synthesis to Macro LearningUte Schmid, Fritz Wysotzki. aips 2000: 371-378

Inference and Visualization of Spatial RelationsSylvia Wiebrock, Lars Wittenburg, Ute Schmid, Fritz Wysotzki. spatialCognition 2000: 212-224 [doi]

1999

Comparing Structures Using a Hopfield-Style Neural NetworkKristina Schädler, Fritz Wysotzki. apin, 11(1):15-30, 1999.

1998

Reference Frames for Spatial Inference in Text UnderstandingBerry Claus, Klaus Eyferth, Carsten Gips, Robin Hörnig, Ute Schmid, Sylvia Wiebrock, Fritz Wysotzki. spatialCognition 1998: 241-266 [doi]

Induction of Recursive Program SchemesUte Schmid, Fritz Wysotzki. ecml 1998: 214-225

Application of a neural net in classification and knowledge discoveryKristina Schädler, Fritz Wysotzki. esann 1998: 117-122 [doi]

1997

A Logical Framework for Graph Theoretical Decision Tree LearningPeter Geibel, Fritz Wysotzki. ilp 1997: 173-180

A Connectionist Approach to Structural Simiarity Determination as a Basis of Clustering, Classification and Feature DetectionKristina Schädler, Fritz Wysotzki. pkdd 1997: 254-264

A Connectionist Approach to the Distance-Based Analysis of Relational DataKristina Schädler, Fritz Wysotzki. ida 1997: 137-148 [doi]

1996

1995

Automatic Synthesis of Control Programs by Combination of Learning and Problem Solving Methods (Extended Abstract)Wolfgang Müller, Fritz Wysotzki. ecml 1995: 323-326

Prototype, Nearest Neighbor and Hybrid Algorithms for Time Series Classification (Extended Abstract)Christel Wisotzki, Fritz Wysotzki. ecml 1995: 364-367

1994

Automatic construction of decision trees for classificationWolfgang Müller 0012, Fritz Wysotzki. anor, 52(4):231-247, 1994. [doi]

The Piecewise Linear Classifier DIPOL92Barbara Schulmeister, Fritz Wysotzki. ecml 1994: 411-414

1986

Program Synthesis by Hierarchical PlanningFritz Wysotzki. aimsa 1986: 3-11

1983

Representation and Induction of Infinite Concepts and Recursive Action SequencesFritz Wysotzki. IJCAI 1983: 409-414

1981

Concept Learning by Structured Examples - An Algebraic ApproachFritz Wysotzki, Werner Kolbe, Joachim Selbig. IJCAI 1981: 153-158

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Publications by 'Fritz Wysotzki'

Viewing Publication 1 - 57 from 57

2009

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1986

1983

1981

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