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Fritz Wysotzki Vis

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*2005
53 Christoph Schmoeger, Carsten Gips, Fritz Wysotzki: Spatial Inference with Constraints. LWA 2005: 228-233
52EEPeter Geibel, Fritz Wysotzki: Risk-Sensitive Reinforcement Learning Applied to Control under Constraints. J. Artif. Intell. Res. (JAIR) 24: 81-108 (2005)
51EEBrijnesh J. Jain, Fritz Wysotzki: Solving inexact graph isomorphism problems using neural networks. Neurocomputing 63: 45-67 (2005)
50EEBrijnesh J. Jain, Peter Geibel, Fritz Wysotzki: SVM learning with the Schur-Hadamard inner product for graphs. Neurocomputing 64: 93-105 (2005)
2004
49EEPeter Geibel, Brijnesh J. Jain, Fritz Wysotzki: SVM learning with the SH inner product. ESANN 2004: 299-304
48EEBrijnesh J. Jain, Fritz Wysotzki: The maximum weighted clique problem and Hopfield networks. ESANN 2004: 331-336
47EEBrijnesh J. Jain, Peter Geibel, Fritz Wysotzki: Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. KI 2004: 241-255
46 Carsten Gips, Fritz Wysotzki: Spatial Inference - Application of Machine Learning Algorithms. LWA 2004: 155-160
45 Brijnesh J. Jain, Fritz Wysotzki: Learning with Neural Networks in the Domain of Graphs. LWA 2004: 163-170
44EEBrijnesh J. Jain, Fritz Wysotzki: Structural Perceptrons for Attributed Graphs. SSPR/SPR 2004: 85-94
43EEPeter Geibel, Fritz Wysotzki: Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent Costs. Appl. Intell. 21(1): 45-56 (2004)
42EEPeter Geibel, Ulf Brefeld, Fritz Wysotzki: Perceptron and SVM learning with generalized cost models. Intell. Data Anal. 8(5): 439-455 (2004)
41EEStefan Bischoff, Fritz Wysotzki: Applied Connectionistic Methods to compare Segmented Images. KI 18(1): 11- (2004)
40EEBrijnesh J. Jain, Fritz Wysotzki: Central Clustering of Attributed Graphs. Machine Learning 56(1-3): 169-207 (2004)
39EEBrijnesh J. Jain, Fritz Wysotzki: Discrimination networks for maximum selection. Neural Networks 17(1): 143-154 (2004)
2003
38EEUlf Brefeld, Peter Geibel, Fritz Wysotzki: Support Vector Machines with Example Dependent Costs. ECML 2003: 23-34
37EEBrijnesh J. Jain, Fritz Wysotzki: An Associative Memory for the Automorphism Group of Structures. ESANN 2003: 107-112
36EEBrijnesh J. Jain, Fritz Wysotzki: A Neural Graph Isomorphism Algorithm based on local Invariants. ESANN 2003: 79-84
35EEBrijnesh J. Jain, Fritz Wysotzki: A Competitive Winner-Takes-All Architecture for Classification and Pattern Recognition of Structures. GbRPR 2003: 259-270
34EEBrijnesh J. Jain, Fritz Wysotzki: A Novel Neural Network Approach to Solve Exact and Inexact Graph Isomorphism Problems. ICANN 2003: 299-306
33 Peter Geibel, Fritz Wysotzki: Perceptron Based Learning with Example Dependent and Noisy Costs. ICML 2003: 218-225
32EEPeter Geibel, Ulf Brefeld, Fritz Wysotzki: Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs. IDA 2003: 167-178
31EECarsten Gips, Fritz Wysotzki: Spatial Inference - Combining Learning and Constraint Solving. KI 2003: 282-296
30EEStefan Bischoff, D. Reuss, Fritz Wysotzki: Applied Connectionistic Methods in Computer Vision to Compare Segmented Images. KI 2003: 312-326
29EEBrijnesh J. Jain, Fritz Wysotzki: A k-Winner-Takes-All Classifier for Structured Data. KI 2003: 342-354
28EEPeter Geibel, Kristina Schädler, Fritz Wysotzki: Connectionist construction of prototypes from decision trees for graph classification. Intell. Data Anal. 7(2): 125-140 (2003)
27 Brijnesh J. Jain, Fritz Wysotzki: Automorphism Partitioning with Neural Networks. Neural Processing Letters 17(2): 205-215 (2003)
2002
26EEEmanuel Kitzelmann, Ute Schmid, Martin Mühlpfordt, Fritz Wysotzki: Inductive Synthesis of Functional Programs. AISC 2002: 26-37
25EEUte Schmid, Marina Müller, Fritz Wysotzki: Integrating Function Application in State-Based Planning. KI 2002: 144-162
24EEBrijnesh J. Jain, Fritz Wysotzki: Fast Winner-Takes-All Networks for the Maximum Clique Problem. KI 2002: 163-173
23EEPeter Geibel, Kristina Schädler, Fritz Wysotzki: Learning of Class Descriptions from Class Discriminations: A Hybrid Approach for Relational Objects. KI 2002: 186-204
22EECarsten Gips, Petra Hofstedt, Fritz Wysotzki: Spatial Inference - Learning vs. Constraint Solving. KI 2002: 299-316
2001
21EEBrijnesh J. Jain, Fritz Wysotzki: On the short-term-memory of WTA nets. ESANN 2001: 289-294
20EEBrijnesh J. Jain, Fritz Wysotzki: Efficient Pattern Discrimination with Inhibitory WTA Nets. ICANN 2001: 827-834
2000
19 Ute Schmid, Fritz Wysotzki: Applying Inductive Program Synthesis to Macro Learning. AIPS 2000: 371-378
18EESylvia Wiebrock, Lars Wittenburg, Ute Schmid, Fritz Wysotzki: Inference and Visualization of Spatial Relations. Spatial Cognition 2000: 212-224
17EEPeter Geibel, Fritz Wysotzki: Graphbasierte Lernverfahren für relationale Daten. Inform., Forsch. Entwickl. 15(1): 1-15 (2000)
1999
16 Kristina Schädler, Fritz Wysotzki: Comparing Structures Using a Hopfield-Style Neural Network. Appl. Intell. 11(1): 15-30 (1999)
1998
15EEUte Schmid, Fritz Wysotzki: Induction of Recursive Program Schemes. ECML 1998: 214-225
14EEKristina Schädler, Fritz Wysotzki: Application of a neural net in classification and knowledge discovery. ESANN 1998: 117-122
13EEBerry Claus, Klaus Eyferth, Carsten Gips, Robin Hörnig, Ute Schmid, Sylvia Wiebrock, Fritz Wysotzki: Reference Frames for Spatial Inference in Text Understanding. Spatial Cognition 1998: 241-266
1997
12EEKristina Schädler, Fritz Wysotzki: A Connectionist Approach to the Distance-Based Analysis of Relational Data. IDA 1997: 137-148
11 Peter Geibel, Fritz Wysotzki: A Logical Framework for Graph Theoretical Decision Tree Learning. ILP 1997: 173-180
10 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
9 Peter Geibel, Fritz Wysotzki: Relational Learning with Decision Trees. ECAI 1996: 428-432
8 Peter Geibel, Fritz Wysotzki: Learning Relational Concepts with Decision Trees. ICML 1996: 166-174
7 Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki: Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996: 212-228
1995
6EEWolfgang Müller, Fritz Wysotzki: Automatic Synthesis of Control Programs by Combination of Learning and Problem Solving Methods (Extended Abstract). ECML 1995: 323-326
5EEChristel Wisotzki, Fritz Wysotzki: Prototype, Nearest Neighbor and Hybrid Algorithms for Time Series Classification (Extended Abstract). ECML 1995: 364-367
1994
4EEBarbara Schulmeister, Fritz Wysotzki: The Piecewise Linear Classifier DIPOL92. ECML 1994: 411-414
1986
3 Fritz Wysotzki: Program Synthesis by Hierarchical Planning. AIMSA 1986: 3-11
1983
2 Fritz Wysotzki: Representation and Induction of Infinite Concepts and Recursive Action Sequences. IJCAI 1983: 409-414
1981
1 Fritz Wysotzki, Werner Kolbe, Joachim Selbig: Concept Learning by Structured Examples - An Algebraic Approach. IJCAI 1981: 153-158

Coauthor Index

1Stefan Bischoff [30] [41]
2Ulf Brefeld [32] [38] [42]
3Berry Claus [13]
4Klaus Eyferth [13]
5Peter Geibel [8] [9] [11] [17] [23] [28] [32] [33] [38] [42] [43] [47] [49] [50] [52]
6Carsten Gips [13] [22] [31] [46] [53]
7Ralf Herbrich [7]
8Petra Hofstedt [22]
9Robin Hörnig [13]
10Brijnesh J. Jain [20] [21] [24] [27] [29] [34] [35] [36] [37] [39] [40] [44] [45] [47] [48] [49] [50] [51]
11Emanuel Kitzelmann [26]
12Werner Kolbe [1]
13Martin Mühlpfordt [26]
14Marina Müller [25]
15Wolfgang Müller [6]
16D. Reuss [30]
17Kristina Schädler [10] [12] [14] [16] [23] [28]
18Tobias Scheffer [7]
19Ute Schmid [13] [15] [18] [19] [25] [26]
20Christoph Schmoeger [53]
21Barbara Schulmeister [4]
22Joachim Selbig [1]
23Sylvia Wiebrock [13] [18]
24Christel Wisotzki [5]
25Lars Wittenburg [18]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)