Michael Schmitt

Publication Activity (10 Years)

Years Active: 1996-2006
Publications (10 Years): 0

University of Ulm, Germany (PHD)

Education History

University of Ulm, Germany

PHD

Publications

Michael Schmitt, Laura Martignon
On the Complexity of Learning Lexicographic Strategies. J. Mach. Learn. Res. 7 (2006)
Michael Schmitt, Laura Martignon
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?. NIPS (2005)
Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon
Inner Product Spaces for Bayesian Networks. J. Mach. Learn. Res. 6 (2005)
Michael Schmitt
On the Capabilities of Higher-Order Neurons: A Radial Basis Function Approach. Neural Comput. 17 (3) (2005)
Frauke Friedrichs, Michael Schmitt
On the power of Boolean computations in generalized RBF neural networks. Neurocomputing 63 (2005)
Michael Schmitt
Some dichotomy theorems for neural learning problems Electron. Colloquium Comput. Complex. (075) (2004)
Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon
Bayesian Networks and Inner Product Spaces. COLT (2004)
Michael Schmitt
On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions Electron. Colloquium Comput. Complex. (033) (2004)
Michael Schmitt
On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions. IEEE Trans. Neural Networks 15 (5) (2004)
Michael Schmitt
New Designs for the Descartes Rule of Signs. Am. Math. Mon. 111 (2) (2004)
Michael Schmitt
Some Dichotomy Theorems for Neural Learning Problems. J. Mach. Learn. Res. 5 (2004)
Michael Schmitt
An Improved VC Dimension Bound for Sparse Polynomials. COLT (2004)
Michael Schmitt
RBF Neural Networks and Descartes' Rule of Signs. ALT (2002)
Michael Schmitt
Descartes' Rule of Signs for Radial Basis Function Neural Networks. Neural Comput. 14 (12) (2002)
Michael Schmitt
Neural Networks with Local Receptive Fields and Superlinear VC Dimension. Neural Comput. 14 (4) (2002)
Michael Schmitt
On the Complexity of Computing and Learning with Multiplicative Neural Networks. Neural Comput. 14 (2) (2002)
Michael Schmitt
Complexity of Learning for Networks of Spiking Neurons with Nonlinear Synaptic Interactions. ICANN (2001)
Michael Schmitt
Radial Basis Function Neural Networks Have Superlinear VC Dimension. COLT/EuroCOLT (2001)
Michael Schmitt
On using the Poincaré polynomial for calculating the VC dimension of neural networks. Neural Networks 14 (10) (2001)
Michael Schmitt
Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension. ICANN (2001)
Michael Schmitt
Neural Networks with Local Receptive Fields and Superlinear VC Dimension Electron. Colloquium Comput. Complex. 8 (45) (2001)
Michael Schmitt
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks Electron. Colloquium Comput. Complex. 7 (2) (2000)
Michael Schmitt
On the Complexity of Computing and Learning with Multiplicative Neural Networks Electron. Colloquium Comput. Complex. 7 (86) (2000)
Michael Schmitt
VC Dimension Bounds for Product Unit Networks. IJCNN (4) (2000)
Michael Schmitt
On the Sample Complexity for Nonoverlapping Neural Networks Electron. Colloquium Comput. Complex. 6 (5) (1999)
Michael Schmitt
On the Sample Complexity for Nonoverlapping Neural Networks. Mach. Learn. 37 (2) (1999)
Wolfgang Maass, Michael Schmitt
On the Complexity of Learning for Spiking Neurons with Temporal Coding. Inf. Comput. 153 (1) (1999)
Michael Schmitt
VC dimension bounds for networks of spiking neurons. ESANN (1999)
Laura Martignon, Michael Schmitt
Simplicity and Robustness of Fast and Frugal Heuristics. Minds Mach. 9 (4) (1999)
Michael Schmitt
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks. NIPS (1999)
Michael Schmitt
On Computing Boolean Functions by a Spiking Neuron. Ann. Math. Artif. Intell. 24 (1-4) (1998)
Michael Schmitt
On the Sample Complexity for Neural Trees. ALT (1998)
Berthold Ruf, Michael Schmitt
Self-organization of spiking neurons using action potential timing. IEEE Trans. Neural Networks 9 (3) (1998)
Michael Schmitt
Identification Criteria and Lower Bounds for Perceptron-LikeLearning Rules. Neural Comput. 10 (1) (1998)
Wolfgang Maass, Michael Schmitt
On the Complexity of Learning for Spiking Neurons with Temporal Coding Electron. Colloquium Comput. Complex. 4 (49) (1997)
Berthold Ruf, Michael Schmitt
Hebbian Learning in Networks of Spiking Neurons Using Temporal Coding. IWANN (1997)
Wolfgang Maass, Michael Schmitt
On the Complexity of Learning for a Spiking Neuron (Extended Abstract). COLT (1997)
Berthold Ruf, Michael Schmitt
Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding. ICANN (1997)
Berthold Ruf, Michael Schmitt
Learning Temporally Encoded Patterns in Networks of Spiking Neurons. Neural Process. Lett. 5 (1) (1997)
Michael Schmitt
Proving Hardness of Neural Network Training Problems. Neural Networks 10 (8) (1997)
Thomas Natschläger, Michael Schmitt
Erratum: Exact VC-Dimension of Boolean Monomials. Inf. Process. Lett. 60 (2) (1996)
Thomas Natschläger, Michael Schmitt
Exact VC-Dimension of Boolean Monomials. Inf. Process. Lett. 59 (1) (1996)

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Publication Activity (10 Years)

Michael Schmitt