Peter Geibel

2008
24	EE	Kai-Uwe Kühnberger, Helmar Gust, Peter Geibel: Perspectives of Neuro--Symbolic Integration -- Extended Abstract --. Recurrent Neural Networks 2008
2007
23	EE	Peter Geibel, Ulf Krumnack, Olga Pustylnikov, Alexander Mehler, Helmar Gust, Kai-Uwe Kühnberger: Structure-Sensitive Learning of Text Types. Australian Conference on Artificial Intelligence 2007: 642-646
22	EE	Kai-Uwe Kühnberger, Tonio Wandmacher, Angela Schwering, Ekaterina Ovchinnikova, Ulf Krumnack, Helmar Gust, Peter Geibel: Modeling Human-Level Intelligence by Integrated Cognition in a Hybrid Architecture. FAInt 2007
21	EE	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. MICAI 2007: 203-214
20	EE	Florian Halbritter, Peter Geibel: Learning Models of Relational MDPs Using Graph Kernels. MICAI 2007: 409-419
19	EE	Peter Geibel, Helmar Gust, Kai-Uwe Kühnberger: Variants of Tree Kernels for XML Documents. MICAI 2007: 850-860
18	EE	Helmar Gust, Kai-Uwe Kühnberger, Peter Geibel: Learning Models of Predicate Logical Theories with Neural Networks Based on Topos Theory. Perspectives of Neural-Symbolic Integration 2007: 233-264
2006
17	EE	Peter Geibel: Reinforcement Learning for MDPs with Constraints. ECML 2006: 646-653
2005
16	EE	Peter Geibel, Fritz Wysotzki: Risk-Sensitive Reinforcement Learning Applied to Control under Constraints. J. Artif. Intell. Res. (JAIR) 24: 81-108 (2005)
15	EE	Brijnesh J. Jain, Peter Geibel, Fritz Wysotzki: SVM learning with the Schur-Hadamard inner product for graphs. Neurocomputing 64: 93-105 (2005)
2004
14	EE	Peter Geibel, Brijnesh J. Jain, Fritz Wysotzki: SVM learning with the SH inner product. ESANN 2004: 299-304
13	EE	Brijnesh J. Jain, Peter Geibel, Fritz Wysotzki: Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. KI 2004: 241-255
12	EE	Peter Geibel, Fritz Wysotzki: Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent Costs. Appl. Intell. 21(1): 45-56 (2004)
11	EE	Peter Geibel, Ulf Brefeld, Fritz Wysotzki: Perceptron and SVM learning with generalized cost models. Intell. Data Anal. 8(5): 439-455 (2004)
2003
10	EE	Ulf Brefeld, Peter Geibel, Fritz Wysotzki: Support Vector Machines with Example Dependent Costs. ECML 2003: 23-34
9		Peter Geibel, Fritz Wysotzki: Perceptron Based Learning with Example Dependent and Noisy Costs. ICML 2003: 218-225
8	EE	Peter Geibel, Ulf Brefeld, Fritz Wysotzki: Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs. IDA 2003: 167-178
7	EE	Peter Geibel, Kristina Schädler, Fritz Wysotzki: Connectionist construction of prototypes from decision trees for graph classification. Intell. Data Anal. 7(2): 125-140 (2003)
2002
6	EE	Peter Geibel, Kristina Schädler, Fritz Wysotzki: Learning of Class Descriptions from Class Discriminations: A Hybrid Approach for Relational Objects. KI 2002: 186-204
2001
5		Peter Geibel: Reinforcement Learning with Bounded Risk. ICML 2001: 162-169
2000
4	EE	Peter Geibel, Fritz Wysotzki: Graphbasierte Lernverfahren für relationale Daten. Inform., Forsch. Entwickl. 15(1): 1-15 (2000)
1997
3		Peter Geibel, Fritz Wysotzki: A Logical Framework for Graph Theoretical Decision Tree Learning. ILP 1997: 173-180
1996
2		Peter Geibel, Fritz Wysotzki: Relational Learning with Decision Trees. ECAI 1996: 428-432
1		Peter Geibel, Fritz Wysotzki: Learning Relational Concepts with Decision Trees. ICML 1996: 166-174

Coauthor Index

1	Ulf Brefeld	[8] [10] [11]
2	Helmar Gust	[18] [19] [21] [22] [23] [24]
3	Florian Halbritter	[20]
4	Brijnesh J. Jain	[13] [14] [15]
5	Ulf Krumnack	[21] [22] [23]
6	Kai-Uwe Kühnberger	[18] [19] [21] [22] [23] [24]
7	Alexander Mehler	[23]
8	Ekaterina Ovchinnikova	[21] [22]
9	Olga Pustylnikov	[23]
10	Kristina Schädler	[6] [7]
11	Angela Schwering	[21] [22]
12	Tonio Wandmacher	[21] [22]
13	Fritz Wysotzki	[1] [2] [3] [4] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

Colors in the list of coauthors