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Fritz Wysotzki
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2010 – 2019
- 2011
- [i1]Peter Geibel, Fritz Wysotzki:
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints. CoRR abs/1109.2147 (2011)
2000 – 2009
- 2009
- [j15]Fritz Wysotzki, Peter Geibel:
A New Information Measure Based on Example-Dependent Misclassification Costs and Its Application in Decision Tree Learning. Adv. Artif. Intell. 2009: 134807:1-134807:13 (2009) - 2005
- [j14]Brijnesh J. Jain, Fritz Wysotzki:
Solving inexact graph isomorphism problems using neural networks. Neurocomputing 63: 45-67 (2005) - [j13]Brijnesh J. Jain, Peter Geibel, Fritz Wysotzki:
SVM learning with the Schur-Hadamard inner product for graphs. Neurocomputing 64: 93-105 (2005) - [j12]Peter Geibel, Fritz Wysotzki:
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints. J. Artif. Intell. Res. 24: 81-108 (2005) - [c42]Fritz Wysotzki:
Development of Inductive Synthesis of Functional Programs. AAIP 2005: 13 - [c41]Christoph Schmoeger, Carsten Gips, Fritz Wysotzki:
Spatial Inference with Constraints. LWA 2005: 228-233 - 2004
- [j11]Peter Geibel, Fritz Wysotzki:
Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent Costs. Appl. Intell. 21(1): 45-56 (2004) - [j10]Peter Geibel, Ulf Brefeld, Fritz Wysotzki:
Perceptron and SVM learning with generalized cost models. Intell. Data Anal. 8(5): 439-455 (2004) - [j9]Stefan Bischoff, Fritz Wysotzki:
Applied Connectionistic Methods to compare Segmented Images. Künstliche Intell. 18(1): 11- (2004) - [j8]Brijnesh J. Jain, Fritz Wysotzki:
Central Clustering of Attributed Graphs. Mach. Learn. 56(1-3): 169-207 (2004) - [j7]Brijnesh J. Jain, Fritz Wysotzki:
Discrimination networks for maximum selection. Neural Networks 17(1): 143-154 (2004) - [c40]Peter Geibel, Brijnesh J. Jain, Fritz Wysotzki:
SVM learning with the SH inner product. ESANN 2004: 299-304 - [c39]Brijnesh J. Jain, Fritz Wysotzki:
The maximum weighted clique problem and Hopfield networks. ESANN 2004: 331-336 - [c38]Brijnesh J. Jain, Peter Geibel, Fritz Wysotzki:
Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. KI 2004: 241-255 - [c37]Carsten Gips, Fritz Wysotzki:
Spatial Inference - Application of Machine Learning Algorithms. LWA 2004: 155-160 - [c36]Brijnesh J. Jain, Fritz Wysotzki:
Learning with Neural Networks in the Domain of Graphs. LWA 2004: 163-170 - [c35]Brijnesh J. Jain, Fritz Wysotzki:
Structural Perceptrons for Attributed Graphs. SSPR/SPR 2004: 85-94 - 2003
- [j6]Peter Geibel, Kristina Schädler, Fritz Wysotzki:
Connectionist construction of prototypes from decision trees for graph classification. Intell. Data Anal. 7(2): 125-140 (2003) - [j5]Brijnesh J. Jain, Fritz Wysotzki:
Automorphism Partitioning with Neural Networks. Neural Process. Lett. 17(2): 205-215 (2003) - [c34]Ulf Brefeld, Peter Geibel, Fritz Wysotzki:
Support Vector Machines with Example Dependent Costs. ECML 2003: 23-34 - [c33]Brijnesh J. Jain, Fritz Wysotzki:
A Neural Graph Isomorphism Algorithm based on local Invariants. ESANN 2003: 79-84 - [c32]Brijnesh J. Jain, Fritz Wysotzki:
An Associative Memory for the Automorphism Group of Structures. ESANN 2003: 107-112 - [c31]Brijnesh J. Jain, Fritz Wysotzki:
A Competitive Winner-Takes-All Architecture for Classification and Pattern Recognition of Structures. GbRPR 2003: 259-270 - [c30]Brijnesh J. Jain, Fritz Wysotzki:
A Novel Neural Network Approach to Solve Exact and Inexact Graph Isomorphism Problems. ICANN 2003: 299-306 - [c29]Peter Geibel, Fritz Wysotzki:
Perceptron Based Learning with Example Dependent and Noisy Costs. ICML 2003: 218-225 - [c28]Peter Geibel, Ulf Brefeld, Fritz Wysotzki:
Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs. IDA 2003: 167-178 - [c27]Carsten Gips, Fritz Wysotzki:
Spatial Inference - Combining Learning and Constraint Solving. KI 2003: 282-296 - [c26]Stefan Bischoff, D. Reuss, Fritz Wysotzki:
Applied Connectionistic Methods in Computer Vision to Compare Segmented Images. KI 2003: 312-326 - [c25]Brijnesh J. Jain, Fritz Wysotzki:
A k-Winner-Takes-All Classifier for Structured Data. KI 2003: 342-354 - 2002
- [c24]Emanuel Kitzelmann, Ute Schmid, Martin Mühlpfordt, Fritz Wysotzki:
Inductive Synthesis of Functional Programs. AISC 2002: 26-37 - [c23]Ute Schmid
, Marina Müller, Fritz Wysotzki:
Integrating Function Application in State-Based Planning. KI 2002: 144-162 - [c22]Brijnesh J. Jain, Fritz Wysotzki:
Fast Winner-Takes-All Networks for the Maximum Clique Problem. KI 2002: 163-173 - [c21]Peter Geibel, Kristina Schädler, Fritz Wysotzki:
Learning of Class Descriptions from Class Discriminations: A Hybrid Approach for Relational Objects. KI 2002: 186-204 - [c20]Carsten Gips, Petra Hofstedt, Fritz Wysotzki:
Spatial Inference - Learning vs. Constraint Solving. KI 2002: 299-316 - 2001
- [c19]Brijnesh J. Jain, Fritz Wysotzki:
On the short-term-memory of WTA nets. ESANN 2001: 289-294 - [c18]Brijnesh J. Jain, Fritz Wysotzki:
Efficient Pattern Discrimination with Inhibitory WTA Nets. ICANN 2001: 827-834 - 2000
- [j4]Peter Geibel, Fritz Wysotzki:
Graphbasierte Lernverfahren für relationale Daten. Inform. Forsch. Entwickl. 15(1): 1-15 (2000) - [c17]Ute Schmid, Fritz Wysotzki:
Applying Inductive Program Synthesis to Macro Learning. AIPS 2000: 371-378 - [c16]Sylvia Wiebrock, Lars Wittenburg, Ute Schmid, Fritz Wysotzki:
Inference and Visualization of Spatial Relations. Spatial Cognition 2000: 212-224
1990 – 1999
- 1999
- [j3]Kristina Schädler, Fritz Wysotzki:
Comparing Structures Using a Hopfield-Style Neural Network. Appl. Intell. 11(1): 15-30 (1999) - 1998
- [c15]Ute Schmid
Induction of Recursive Program Schemes. ECML 1998: 214-225 - [c14]Kristina Schädler, Fritz Wysotzki:
Application of a neural net in classification and knowledge discovery. ESANN 1998: 117-122 - [c13]Berry Claus
Reference Frames for Spatial Inference in Text Understanding. Spatial Cognition 1998: 241-266 - 1997
- [c12]Kristina Schädler, Fritz Wysotzki:
A Connectionist Approach to the Distance-Based Analysis of Relational Data. IDA 1997: 137-148 - [c11]Peter Geibel, Fritz Wysotzki:
A Logical Framework for Graph Theoretical Decision Tree Learning. ILP 1997: 173-180 - [c10]Kristina Schädler, Fritz Wysotzki:
A Connectionist Approach to Structural Simiarity Determination as a Basis of Clustering, Classification and Feature Detection. PKDD 1997: 254-264 - 1996
- [j2]Marion Finke, Günter Hommel, Tobias Scheffer, Fritz Wysotzki:
Aerial Robotics in Computer Science Education. Comput. Sci. Educ. 7(2): 239-246 (1996) - [c9]Peter Geibel, Fritz Wysotzki:
Relational Learning with Decision Trees. ECAI 1996: 428-432 - [c8]Peter Geibel, Fritz Wysotzki:
Learning Relational Concepts with Decision Trees. ICML 1996: 166-174 - [c7]Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki:
Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996: 212-228 - 1995
- [c6]Wolfgang Müller, Fritz Wysotzki:
Automatic Synthesis of Control Programs by Combination of Learning and Problem Solving Methods (Extended Abstract). ECML 1995: 323-326 - [c5]Christel Wisotzki, Fritz Wysotzki:
Prototype, Nearest Neighbor and Hybrid Algorithms for Time Series Classification (Extended Abstract). ECML 1995: 364-367 - 1994
- [j1]Wolfgang Müller, Fritz Wysotzki:
Automatic construction of decision trees for classification. Ann. Oper. Res. 52(4): 231-247 (1994) - [c4]Barbara Schulmeister, Fritz Wysotzki:
The Piecewise Linear Classifier DIPOL92. ECML 1994: 411-414
1980 – 1989
- 1986
- [c3]Fritz Wysotzki:
Program Synthesis by Hierarchical Planning. AIMSA 1986: 3-11 - 1983
- [c2]Fritz Wysotzki:
Representation and Induction of Infinite Concepts and Recursive Action Sequences. IJCAI 1983: 409-414 - 1981
- [c1]Fritz Wysotzki, Werner Kolbe, Joachim Selbig:
Concept Learning by Structured Examples - An Algebraic Approach. IJCAI 1981: 153-158
Coauthor Index
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