Michael Schmitt

According to our database, Michael Schmitt authored at least 36 papers between 1994 and 2006.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2006

On the Complexity of Learning Lexicographic Strategies.

Michael Schmitt, Laura Martignon

J. Mach. Learn. Res., 2006

2005

On the Capabilities of Higher-Order Neurons: A Radial Basis Function Approach.

Michael Schmitt

Neural Comput., 2005

Inner Product Spaces for Bayesian Networks.

Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon

J. Mach. Learn. Res., 2005

On the power of Boolean computations in generalized RBF neural networks.

Frauke Friedrichs, Michael Schmitt

Neurocomputing, 2005

On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?.

Michael Schmitt, Laura Martignon

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004

New Designs for the Descartes Rule of Signs.

Michael Schmitt

Am. Math. Mon., 2004

Some dichotomy theorems for neural learning problems

Michael Schmitt

Electron. Colloquium Comput. Complex., 2004

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions

Michael Schmitt

Electron. Colloquium Comput. Complex., 2004

An Improved VC Dimension Bound for Sparse Polynomials.

Michael Schmitt

Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

Bayesian Networks and Inner Product Spaces.

Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon

Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2002

Descartes' Rule of Signs for Radial Basis Function Neural Networks.

Michael Schmitt

Neural Comput., 2002

Neural Networks with Local Receptive Fields and Superlinear VC Dimension.

Michael Schmitt

Neural Comput., 2002

On the Complexity of Computing and Learning with Multiplicative Neural Networks.

Michael Schmitt

Neural Comput., 2002

RBF Neural Networks and Descartes' Rule of Signs.

Michael Schmitt

Proceedings of the Algorithmic Learning Theory, 13th International Conference, 2002

2001

On using the Poincaré polynomial for calculating the VC dimension of neural networks.

Michael Schmitt

Neural Networks, 2001

Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension.

Michael Schmitt

Proceedings of the Artificial Neural Networks, 2001

Complexity of Learning for Networks of Spiking Neurons with Nonlinear Synaptic Interactions.

Michael Schmitt

Proceedings of the Artificial Neural Networks, 2001

Radial Basis Function Neural Networks Have Superlinear VC Dimension.

Michael Schmitt

Proceedings of the Computational Learning Theory, 2001

2000

VC Dimension Bounds for Product Unit Networks.

Michael Schmitt

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

1999

Simplicity and Robustness of Fast and Frugal Heuristics.

Laura Martignon, Michael Schmitt

Minds Mach., 1999

On the Sample Complexity for Nonoverlapping Neural Networks

Michael Schmitt

Electron. Colloquium Comput. Complex., 1999

Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks.

Michael Schmitt

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

VC dimension bounds for networks of spiking neurons.

Michael Schmitt

Proceedings of the 7th European Symposium on Artificial Neural Networks, 1999

1998

Self-organization of spiking neurons using action potential timing.

Berthold Ruf, Michael Schmitt

IEEE Trans. Neural Networks, 1998

Identification Criteria and Lower Bounds for Perceptron-LikeLearning Rules.

Michael Schmitt

Neural Comput., 1998

On Computing Boolean Functions by a Spiking Neuron.

Michael Schmitt

Ann. Math. Artif. Intell., 1998

On the Sample Complexity for Neural Trees.

Michael Schmitt

Proceedings of the Algorithmic Learning Theory, 9th International Conference, 1998

1997

Learning Temporally Encoded Patterns in Networks of Spiking Neurons.

Berthold Ruf, Michael Schmitt

Neural Process. Lett., 1997

Proving Hardness of Neural Network Training Problems.

Michael Schmitt

Neural Networks, 1997

On the Complexity of Learning for Spiking Neurons with Temporal Coding

Wolfgang Maass, Michael Schmitt

Electron. Colloquium Comput. Complex., 1997

Hebbian Learning in Networks of Spiking Neurons Using Temporal Coding.

Berthold Ruf, Michael Schmitt

Proceedings of the Biological and Artificial Computation: From Neuroscience to Technology, 1997

Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding.

Berthold Ruf, Michael Schmitt

Proceedings of the Artificial Neural Networks, 1997

On the Complexity of Learning for a Spiking Neuron (Extended Abstract).

Wolfgang Maass, Michael Schmitt

Proceedings of the Tenth Annual Conference on Computational Learning Theory, 1997

1996

Erratum: Exact VC-Dimension of Boolean Monomials.

Thomas Natschläger, Michael Schmitt

Inf. Process. Lett., 1996

Exact VC-Dimension of Boolean Monomials.

Thomas Natschläger, Michael Schmitt

Inf. Process. Lett., 1996

1994

Komplexität neuronaler Lernprobleme.

Michael Schmitt

PhD thesis, 1994