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ORCIDMichael Schmitt 0001
Person information
- affiliation: Ruhr University Bochum, Germany
- affiliation (former): Graz University of Technology, Austria
- affiliation (PhD 1994): University of Ulm, Germany
Other persons with the same name
- Michael Schmitt — disambiguation page
- Michael Gerz (aka: Michael Schmitt 0002) — Fraunhofer Institute for Communication, Information Processing, and Ergonomics FKIE, Wachtberg, Germany
- Michael Schmitt 0003
![[0000-0002-0575-2362]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-0575-2362")
— Bundeswehr University Munich, Neubiberg, Germany - Michael Schmitt 0004 — Public Research Centre Henri Tudor, Luxembourg
- Michael Schmitt 0005 — Rheinisch-Westfälische Technische Hochschule Aachen, RWTH, Department of Technical Computer Science, Germany
- Michael Schmitt 0006 — Visometry GmbH, Darmstadt, Germany (and 1 more)
- Michael Schmitt 0007 — Friedrich Schiller University Jena, Institute of Physical Chemistry, IPC, Abbe Center of Photonics, ACP, LPI, Germany
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Books and Theses
- 1994
[b1]Michael Schmitt:
Komplexität neuronaler Lernprobleme. University of Ulm, Germany, Lang 1994, ISBN 978-3-631-49906-1, pp. 1-165
Journal Articles
- 2006
- [j21]Michael Schmitt, Laura Martignon:
On the Complexity of Learning Lexicographic Strategies. J. Mach. Learn. Res. 7: 55-83 (2006) - 2005
- [j20]Frauke Friedrichs, Michael Schmitt:
On the power of Boolean computations in generalized RBF neural networks. Neurocomputing 63: 483-498 (2005) - [j19]Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon:
Inner Product Spaces for Bayesian Networks. J. Mach. Learn. Res. 6: 1383-1403 (2005) - [j18]Michael Schmitt:
On the Capabilities of Higher-Order Neurons: A Radial Basis Function Approach. Neural Comput. 17(3): 715-729 (2005) - 2004
- [j17]Michael Schmitt:
Some Dichotomy Theorems for Neural Learning Problems. J. Mach. Learn. Res. 5: 891-912 (2004) - [j16]Michael Schmitt:
New Designs for the Descartes Rule of Signs. Am. Math. Mon. 111(2): 159-164 (2004) - [j15]Michael Schmitt:
On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions. IEEE Trans. Neural Networks 15(5): 995-1001 (2004) - 2002
- [j14]Michael Schmitt:
On the Complexity of Computing and Learning with Multiplicative Neural Networks. Neural Comput. 14(2): 241-301 (2002) - [j13]Michael Schmitt:
Neural Networks with Local Receptive Fields and Superlinear VC Dimension. Neural Comput. 14(4): 919-956 (2002) - [j12]Michael Schmitt:
Descartes' Rule of Signs for Radial Basis Function Neural Networks. Neural Comput. 14(12): 2997-3011 (2002) - 2001
- [j11]Michael Schmitt:
On using the Poincaré polynomial for calculating the VC dimension of neural networks. Neural Networks 14(10): 1465- (2001) - 1999
- [j10]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for Spiking Neurons with Temporal Coding. Inf. Comput. 153(1): 26-46 (1999) - [j9]Laura Martignon, Michael Schmitt:
Simplicity and Robustness of Fast and Frugal Heuristics. Minds Mach. 9(4): 565-593 (1999) - [j8]Michael Schmitt:
On the Sample Complexity for Nonoverlapping Neural Networks. Mach. Learn. 37(2): 131-141 (1999) - 1998
- [j7]Michael Schmitt:
On Computing Boolean Functions by a Spiking Neuron. Ann. Math. Artif. Intell. 24(1-4): 181-191 (1998) - [j6]Michael Schmitt:
Identification Criteria and Lower Bounds for Perceptron-LikeLearning Rules. Neural Comput. 10(1): 235-250 (1998) - [j5]Berthold Ruf, Michael Schmitt:
Self-organization of spiking neurons using action potential timing. IEEE Trans. Neural Networks 9(3): 575-578 (1998) - 1997
- [j4]Michael Schmitt:
Proving Hardness of Neural Network Training Problems. Neural Networks 10(8): 1533-1534 (1997) - [j3]Berthold Ruf, Michael Schmitt:
Learning Temporally Encoded Patterns in Networks of Spiking Neurons. Neural Process. Lett. 5(1): 9-18 (1997) - 1996
- [j2]Thomas Natschläger, Michael Schmitt:
Exact VC-Dimension of Boolean Monomials. Inf. Process. Lett. 59(1): 19-20 (1996) - [j1]Thomas Natschläger, Michael Schmitt:
Erratum: Exact VC-Dimension of Boolean Monomials. Inf. Process. Lett. 60(2): 107 (1996)
Conference and Workshop Papers
- 2005
- [c14]Michael Schmitt, Laura Martignon:
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?. NIPS 2005: 1177-1184 - 2004
- [c13]Michael Schmitt:
An Improved VC Dimension Bound for Sparse Polynomials. COLT 2004: 393-407 - [c12]Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon:
Bayesian Networks and Inner Product Spaces. COLT 2004: 518-533 - 2002
- [c11]Michael Schmitt:
RBF Neural Networks and Descartes' Rule of Signs. ALT 2002: 321-335 - 2001
- [c10]Michael Schmitt:
Radial Basis Function Neural Networks Have Superlinear VC Dimension. COLT/EuroCOLT 2001: 14-30 - [c9]Michael Schmitt:
Complexity of Learning for Networks of Spiking Neurons with Nonlinear Synaptic Interactions. ICANN 2001: 247-252 - [c8]Michael Schmitt:
Product Unit Neural Networks with Constant Depth and Superlinear VC Dimension. ICANN 2001: 253-258 - 2000
- [c7]Michael Schmitt:
VC Dimension Bounds for Product Unit Networks. IJCNN (4) 2000: 165-170 - 1999
- [c6]Michael Schmitt:
VC dimension bounds for networks of spiking neurons. ESANN 1999: 429-434 - [c5]Michael Schmitt:
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks. NIPS 1999: 328-334 - 1998
- [c4]Michael Schmitt:
On the Sample Complexity for Neural Trees. ALT 1998: 375-384 - 1997
- [c3]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for a Spiking Neuron (Extended Abstract). COLT 1997: 54-61 - [c2]Berthold Ruf, Michael Schmitt:
Unsupervised Learning in Networks of Spiking Neurons Using Temporal Coding. ICANN 1997: 361-366 - [c1]Berthold Ruf, Michael Schmitt:
Hebbian Learning in Networks of Spiking Neurons Using Temporal Coding. IWANN 1997: 380-389
Informal and Other Publications
- 2004
- [i7]Michael Schmitt:
On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions. Electron. Colloquium Comput. Complex. TR04 (2004) - [i6]Michael Schmitt:
Some dichotomy theorems for neural learning problems. Electron. Colloquium Comput. Complex. TR04 (2004) - 2001
- [i5]Michael Schmitt:
Neural Networks with Local Receptive Fields and Superlinear VC Dimension. Electron. Colloquium Comput. Complex. TR01 (2001) - 2000
- [i4]Michael Schmitt:
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks. Electron. Colloquium Comput. Complex. TR00 (2000) - [i3]Michael Schmitt:
On the Complexity of Computing and Learning with Multiplicative Neural Networks. Electron. Colloquium Comput. Complex. TR00 (2000) - 1999
- [i2]Michael Schmitt:
On the Sample Complexity for Nonoverlapping Neural Networks. Electron. Colloquium Comput. Complex. TR99 (1999) - 1997
- [i1]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for Spiking Neurons with Temporal Coding. Electron. Colloquium Comput. Complex. TR97 (1997)
Coauthor Index
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