F. Wysotzki

发表

Risk-Sensitive Reinforcement Learning Applied to Control under Constraints

Fritz Wysotzki, Peter Geibel, Peter Geibel, 2005, J. Artif. Intell. Res..

II Graph Based Induction of Spatial Relations in Mental Models

Fritz Wysotzki, Peter Geibel, Carsten Gips, 2007 .

Theoretical Foundations of a Special Neural Net Approach for Graphmatching

F. Wysotzki, K. Schädler, 1997 .

Grenander, U., Pattern Synthesis, Lectures in Pattern Theory I, Berlin‐Heidelberg‐New York. Springer‐Verlag. 1976. VII, 509 S., 120 Abb., DM 36,20. US $ 14.80. (Appl. Math. Sciences 18) .

F. Wysotzki, 1977 .

Learning Relational Concepts with Decision Trees

Fritz Wysotzki, Peter Geibel, Peter Geibel, 1996, ICML.

Reference Frames for Spatial Inference in Text Understanding

Fritz Wysotzki, Ute Schmid, Carsten Gips, 1998, Spatial Cognition.

Competitive Winner-Takes-All Clustering in the Domain of Graphs

F. Wysotzki, B. Jain, 2007 .

Using costs varying from object to object to construct linear and piecewise linear classi ers

Peter Geibel, F. Wysotzki, 2002 .

Do Referen e Frames in Language Map onto Those ofMental Models ?

F. Wysotzki, K. Eyferth, Ute S hmid, 2000 .

Simultaneous Planning for Sets of Initial

F. Wysotzki, StatesUte Schmid, Olaf Brandes, 1997 .

Solving Constraints with Trigonometric Functions Occurring in the Workspace of a Mobile Robot by Methods of Machine Learning

F. Wysotzki, Carsten Gips, Sylvia Wiebrock, 2000 .

INDUCTION OF RELATIONAL DECISION TREES BY OP- TIMIZATION OF STRUCTURAL ATTRIBUTES

F. Wysotzki, 1997 .

Eecient Algorithms for -subsumption

R. Herbrich, F. Wysotzki, Tobias Scheeer, 2022 .

Inference and Visualization of Spatial Relations

Fritz Wysotzki, Ute Schmid, Lars Wittenburg, 2000, Spatial Cognition.

A measurement of the lifetime of the Λ5He hypernucleus

F. Wysotzki, J. Wickens, G. Bertrand-Coremans, 1970 .

Learning of Class Des riptions from ClassDis riminations : A Hybrid Approa h forRelational Obje

F. Wysotzki, tsPeter, Geibel, 2002 .

A New Distance Measure for Segmented Images Based on MPEG-7 Descriptors

F. Wysotzki, Heinri h-Hertz-Institute, 2002 .

The maximum weighted clique problem and Hopfield networks

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2004, ESANN.

A new determination of the binding-energy values of the light hypernuclei (A ≤ 15)

F. Wysotzki, G. Wilquet, T. Tymieniecka, 1973 .

On the lifetime of the 3?H ypernucleus

F. Wysotzki, J. Wickens, G. Wilquet, 1970 .

Some properties of the charged Σ hyperons

F. Wysotzki, J. Wickens, J. Simonovic, 1971 .

Application of a neural net in classification and knowledge discovery

Kristina Schädler, Fritz Wysotzki, F. Wysotzki, 1998, ESANN.

Distance-basedClassificationof Structureswithin a Connectionist Framework

F. Wysotzki, B. Jain, 2001 .

Multi-layer perceptron learning in the domain of attributed graphs

F. Wysotzki, B.J. Jain, F. Wysotzki, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

Perceptron learning in the domain of graphs

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

Computer-Supported Decision Making with Object Dependent Costs for Misclassifications

Fritz Wysotzki, Peter Geibel, Peter Geibel, 2006 .

Support Vector Machines with Example Dependent Costs

Ulf Brefeld, Fritz Wysotzki, Peter Geibel, 2003, ECML.

Learning Linear Classifiers Sensitive to Example Dependent and Noisy Costs

Ulf Brefeld, Fritz Wysotzki, Peter Geibel, 2003, IDA.

SVM learning with the Schur-Hadamard inner product for graphs

Fritz Wysotzki, Brijnesh J. Jain, Peter Geibel, 2005, Neurocomputing.

Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition

Fritz Wysotzki, Brijnesh J. Jain, Peter Geibel, 2004, KI.

Solving inexact graph isomorphism problems using neural networks

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2005, Neurocomputing.

Comparing Structures Using a Hopfield-Style Neural Network

Kristina Schädler, Fritz Wysotzki, F. Wysotzki, 1999, Applied Intelligence.

A k-Winner-Takes-All Classifier for Structured Data

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2003, KI.

A Competitive Winner-Takes-All Architecture for Classification and Pattern Recognition of Structures

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2003, GbRPR.

Central Clustering of Attributed Graphs

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2004, Machine Learning.

Discrimination networks for maximum selection

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2004, Neural Networks.

Efficient Pattern Discrimination with Inhibitory WTA Nets

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2001, ICANN.

On the short-term-memory of WTA nets

Fritz Wysotzki, Brijnesh J. Jain, F. Wysotzki, 2001, ESANN.

Spatial Inference and Constraint Solving

Fritz Wysotzki, Carsten Gips, F. Wysotzki, 2002 .

COULOMB DISINTEGRATION OF $lambda$-H$sup 3$ IN NUCLEAR EMULSION.

F. Wysotzki, G. Bohm, 1970 .

Evidence for the existence of particle-unstable states of the Λ12C and Λ14N hypernuclei

F. Wysotzki, J. Wickens, G. Wilquet, 1970 .

A determination of the masses of the charged Σ hyperons

F. Wysotzki, J. Wickens, J. Sacton, 1972 .

The binding energy values of the Λ7Be, Λ10B and Λ11B hypernuclei

F. Wysotzki, J. Wickens, G. Wilquet, 1969 .

The π+ mesonic decays of light hypernuclei

F. Wysotzki, J. Wickens, P. March, 1969 .

Modelling Spatial Inferences in Text Understanding

Fritz Wysotzki, Ute Schmid, Sylvia Wiebrock, 2000 .

Skill acquisition can be regarded as program synthesis: An integrative approach to learning by doing and learning by analogy

Fritz Wysotzki, Ute Schmid, Ute Schmid, 1998 .

Applying Inductive Program Synthesis to Learning Domain-Dependent Control Knowledge - Transforming Plans into Programs

Fritz Wysotzki, Ute Schmid, Ute Schmid, 2000 .

Applying Inductive Program Synthesis to Macro Learning

Fritz Wysotzki, Ute Schmid, Ute Schmid, 2000, AIPS.

Spatial Inference - Learning vs. Constraint Solving

Petra Hofstedt, Fritz Wysotzki, Carsten Gips, 2002, KI.

Perceptron and SVM learning with generalized cost models

Ulf Brefeld, Fritz Wysotzki, Peter Geibel, 2004, Intell. Data Anal..

Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent Costs

Fritz Wysotzki, Peter Geibel, Peter Geibel, 2004, Applied Intelligence.

Prototype, Nearest Neighbor and Hybrid Algorithms for Time Series Classification (Extended Abstract)

Fritz Wysotzki, Christel Wisotzki, F. Wysotzki, 1995, ECML.

Integrating Function Application in State-Based Planning

Fritz Wysotzki, Ute Schmid, Marina Müller, 2002, KI.

Automatic Synthesis of Control Programs by Combination of Learning and Problem Solving Methods (Extended Abstract)

Fritz Wysotzki, Wolfgang Müller, F. Wysotzki, 1995, ECML.