Michael Schmitt

43 papers A* 3B 3C 3Misc 1Journal 28Unranked 4
YearRankTypeTitle / Venue / Authors
2006 J jnl
On the Complexity of Learning Lexicographic Strategies.
J. Mach. Learn. Res.
Michael Schmitt, Laura Martignon
2005 J jnl
Inner Product Spaces for Bayesian Networks.
J. Mach. Learn. Res.
Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon
2005 — conf
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?.
NIPS
Michael Schmitt, Laura Martignon
2005 J jnl
On the Capabilities of Higher-Order Neurons: A Radial Basis Function Approach.
Neural Comput.
Michael Schmitt
2005 J jnl
On the power of Boolean computations in generalized RBF neural networks.
Neurocomputing
Frauke Friedrichs, Michael Schmitt
2004 A* conf
An Improved VC Dimension Bound for Sparse Polynomials.
COLT
Michael Schmitt
2004 A* conf
Bayesian Networks and Inner Product Spaces.
COLT
Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon
2004 J jnl
New Designs for the Descartes Rule of Signs.
Am. Math. Mon.
Michael Schmitt
2004 J jnl
On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions
Electron. Colloquium Comput. Complex.
Michael Schmitt
2004 J jnl
On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.
IEEE Trans. Neural Networks
Michael Schmitt
2004 J jnl
Some Dichotomy Theorems for Neural Learning Problems.
J. Mach. Learn. Res.
Michael Schmitt
2004 J jnl
Some dichotomy theorems for neural learning problems
Electron. Colloquium Comput. Complex.
Michael Schmitt
2002 J jnl
Descartes' Rule of Signs for Radial Basis Function Neural Networks.
Neural Comput.
Michael Schmitt
2002 J jnl
Neural Networks with Local Receptive Fields and Superlinear VC Dimension.
Neural Comput.
Michael Schmitt
2002 J jnl
On the Complexity of Computing and Learning with Multiplicative Neural Networks.
Neural Comput.
Michael Schmitt
2002 B conf
RBF Neural Networks and Descartes' Rule of Signs.
ALT
Michael Schmitt
2001 C conf
Complexity of Learning for Networks of Spiking Neurons with Nonlinear Synaptic Interactions.
ICANN
Michael Schmitt
2001 J jnl
Neural Networks with Local Receptive Fields and Superlinear VC Dimension
Electron. Colloquium Comput. Complex.
Michael Schmitt
2001 J jnl
On using the Poincaré polynomial for calculating the VC dimension of neural networks.
Neural Networks
Michael Schmitt
2001 C conf
Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension.
ICANN
Michael Schmitt
2001 — conf
Radial Basis Function Neural Networks Have Superlinear VC Dimension.
COLT/EuroCOLT
Michael Schmitt
2000 J jnl
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks
Electron. Colloquium Comput. Complex.
Michael Schmitt
2000 J jnl
On the Complexity of Computing and Learning with Multiplicative Neural Networks
Electron. Colloquium Comput. Complex.
Michael Schmitt
2000 — conf
VC Dimension Bounds for Product Unit Networks.
IJCNN (4)
Michael Schmitt
1999 — conf
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks.
NIPS
Michael Schmitt
1999 J jnl
On the Complexity of Learning for Spiking Neurons with Temporal Coding.
Inf. Comput.
Wolfgang Maass, Michael Schmitt
1999 J jnl
On the Sample Complexity for Nonoverlapping Neural Networks
Electron. Colloquium Comput. Complex.
Michael Schmitt
1999 J jnl
On the Sample Complexity for Nonoverlapping Neural Networks.
Mach. Learn.
Michael Schmitt
1999 J jnl
Simplicity and Robustness of Fast and Frugal Heuristics.
Minds Mach.
Laura Martignon, Michael Schmitt
1999 B conf
VC dimension bounds for networks of spiking neurons.
ESANN
Michael Schmitt
1998 J jnl
Identification Criteria and Lower Bounds for Perceptron-LikeLearning Rules.
Neural Comput.
Michael Schmitt
1998 J jnl
On Computing Boolean Functions by a Spiking Neuron.
Ann. Math. Artif. Intell.
Michael Schmitt
1998 B conf
On the Sample Complexity for Neural Trees.
ALT
Michael Schmitt
1998 J jnl
Self-organization of spiking neurons using action potential timing.
IEEE Trans. Neural Networks
Berthold Ruf, Michael Schmitt
1997 Misc conf
Hebbian Learning in Networks of Spiking Neurons Using Temporal Coding.
IWANN
Berthold Ruf, Michael Schmitt
1997 J jnl
Learning Temporally Encoded Patterns in Networks of Spiking Neurons.
Neural Process. Lett.
Berthold Ruf, Michael Schmitt
1997 J jnl
On the Complexity of Learning for Spiking Neurons with Temporal Coding
Electron. Colloquium Comput. Complex.
Wolfgang Maass, Michael Schmitt
1997 A* conf
On the Complexity of Learning for a Spiking Neuron (Extended Abstract).
COLT
Wolfgang Maass, Michael Schmitt
1997 J jnl
Proving Hardness of Neural Network Training Problems.
Neural Networks
Michael Schmitt
1997 C conf
Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding.
ICANN
Berthold Ruf, Michael Schmitt
1996 J jnl
Erratum: Exact VC-Dimension of Boolean Monomials.
Inf. Process. Lett.
Thomas Natschläger, Michael Schmitt
1996 J jnl
Exact VC-Dimension of Boolean Monomials.
Inf. Process. Lett.
Thomas Natschläger, Michael Schmitt
1994 — —
Komplexität neuronaler Lernprobleme.
Michael Schmitt