default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2000 – 2009
- 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) - [c14]Michael Schmitt, Laura Martignon:
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?. NIPS 2005: 1177-1184 - 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) - [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 - [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) - 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) - [c11]Michael Schmitt:
RBF Neural Networks and Descartes' Rule of Signs. ALT 2002: 321-335 - 2001
- [j11]Michael Schmitt:
On using the Poincaré polynomial for calculating the VC dimension of neural networks. Neural Networks 14(10): 1465- (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 - [i5]Michael Schmitt:
Neural Networks with Local Receptive Fields and Superlinear VC Dimension. Electron. Colloquium Comput. Complex. TR01 (2001) - 2000
- [c7]Michael Schmitt:
VC Dimension Bounds for Product Unit Networks. IJCNN (4) 2000: 165-170 - [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)
1990 – 1999
- 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) - [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 - [i2]Michael Schmitt:
On the Sample Complexity for Nonoverlapping Neural Networks. Electron. Colloquium Comput. Complex. TR99 (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) - [c4]Michael Schmitt:
On the Sample Complexity for Neural Trees. ALT 1998: 375-384 - 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) - [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 - [i1]Wolfgang Maass, Michael Schmitt:
On the Complexity of Learning for Spiking Neurons with Temporal Coding. Electron. Colloquium Comput. Complex. TR97 (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) - 1994
- [b1]Michael Schmitt:
Komplexität neuronaler Lernprobleme. University of Ulm, Germany, Lang 1994, ISBN 978-3-631-49906-1, pp. 1-165
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-21 21:23 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
