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Peter Geibel: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Peter Geibel, Fritz Wysotzki
    Relational Learning with Decision Trees. [Citation Graph (0, 0)][DBLP]
    ECAI, 1996, pp:428-432 [Conf]
  2. Ulf Brefeld, Peter Geibel, Fritz Wysotzki
    Support Vector Machines with Example Dependent Costs. [Citation Graph (0, 0)][DBLP]
    ECML, 2003, pp:23-34 [Conf]
  3. Peter Geibel
    Reinforcement Learning for MDPs with Constraints. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:646-653 [Conf]
  4. Peter Geibel, Brijnesh J. Jain, Fritz Wysotzki
    SVM learning with the SH inner product. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:299-304 [Conf]
  5. Peter Geibel
    Reinforcement Learning with Bounded Risk. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:162-169 [Conf]
  6. Peter Geibel, Fritz Wysotzki
    Perceptron Based Learning with Example Dependent and Noisy Costs. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:218-225 [Conf]
  7. Peter Geibel, Fritz Wysotzki
    Learning Relational Concepts with Decision Trees. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:166-174 [Conf]
  8. Peter Geibel, Ulf Brefeld, Fritz Wysotzki
    Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs. [Citation Graph (0, 0)][DBLP]
    IDA, 2003, pp:167-178 [Conf]
  9. Peter Geibel, Fritz Wysotzki
    A Logical Framework for Graph Theoretical Decision Tree Learning. [Citation Graph (0, 0)][DBLP]
    ILP, 1997, pp:173-180 [Conf]
  10. Peter Geibel, Kristina Schädler, Fritz Wysotzki
    Learning of Class Descriptions from Class Discriminations: A Hybrid Approach for Relational Objects. [Citation Graph (0, 0)][DBLP]
    KI, 2002, pp:186-204 [Conf]
  11. Brijnesh J. Jain, Peter Geibel, Fritz Wysotzki
    Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. [Citation Graph (0, 0)][DBLP]
    KI, 2004, pp:241-255 [Conf]
  12. Peter Geibel, Fritz Wysotzki
    Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent Costs. [Citation Graph (0, 0)][DBLP]
    Appl. Intell., 2004, v:21, n:1, pp:45-56 [Journal]
  13. Peter Geibel, Ulf Brefeld, Fritz Wysotzki
    Perceptron and SVM learning with generalized cost models. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2004, v:8, n:5, pp:439-455 [Journal]
  14. Peter Geibel, Kristina Schädler, Fritz Wysotzki
    Connectionist construction of prototypes from decision trees for graph classification. [Citation Graph (0, 0)][DBLP]
    Intell. Data Anal., 2003, v:7, n:2, pp:125-140 [Journal]
  15. Peter Geibel, Fritz Wysotzki
    Graphbasierte Lernverfahren für relationale Daten. [Citation Graph (0, 0)][DBLP]
    Inform., Forsch. Entwickl., 2000, v:15, n:1, pp:1-15 [Journal]
  16. Brijnesh J. Jain, Peter Geibel, Fritz Wysotzki
    SVM learning with the Schur-Hadamard inner product for graphs. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:93-105 [Journal]
  17. Kai-Uwe Kühnberger, Tonio Wandmacher, Angela Schwering, Ekaterina Ovchinnikova, Ulf Krumnack, Helmar Gust, Peter Geibel
    I-Cog: A Computational Framework for Integrated Cognition of Higher Cognitive Abilities. [Citation Graph (0, 0)][DBLP]
    MICAI, 2007, pp:203-214 [Conf]
  18. Peter Geibel, Helmar Gust, Kai-Uwe Kühnberger
    Variants of Tree Kernels for XML Documents. [Citation Graph (0, 0)][DBLP]
    MICAI, 2007, pp:850-860 [Conf]
  19. Florian Halbritter, Peter Geibel
    Learning Models of Relational MDPs Using Graph Kernels. [Citation Graph (0, 0)][DBLP]
    MICAI, 2007, pp:409-419 [Conf]
  20. Peter Geibel, Fritz Wysotzki
    Risk-Sensitive Reinforcement Learning Applied to Control under Constraints. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2005, v:24, n:, pp:81-108 [Journal]

  21. Structure-Sensitive Learning of Text Types. [Citation Graph (, )][DBLP]


  22. Perspectives of Neuro--Symbolic Integration -- Extended Abstract --. [Citation Graph (, )][DBLP]


  23. Learning and Memorizing Models of Logical Theories in a Hybrid Learning Device. [Citation Graph (, )][DBLP]


  24. Classification of Documents Based on the Structure of Their DOM Trees. [Citation Graph (, )][DBLP]


  25. Mining Concept Similarities for Heterogeneous Ontologies. [Citation Graph (, )][DBLP]


  26. Modeling Human-Level Intelligence by Integrated Cognition in a Hybrid Architecture. [Citation Graph (, )][DBLP]


  27. Ontology Mapping via Structural and Instance-based Similarity Measures. [Citation Graph (, )][DBLP]


  28. Variable Selection as an Instance-Based Ontology Mapping Strategy. [Citation Graph (, )][DBLP]


  29. Extensional Ontology Matching with Variable Selection for Support Vector Machines. [Citation Graph (, )][DBLP]


  30. Learning from Inconsistencies in an Integrated Cognitive Architecture. [Citation Graph (, )][DBLP]


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