ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: PLOS ONE (12:3) - San Francisco, Calif. : Public Library of Science (PLOS) - Art.-Id. e0174392
- Event
-
Veröffentlichung
- (where)
-
Berlin
- (who)
-
Technische Universität Berlin
- (when)
-
2020
- Creator
- DOI
-
10.14279/depositonce-10949
- Handle
-
11303/12075
- URN
-
urn:nbn:de:101:1-2020120200580196698945
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:56 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Vidovic, Marina Marie-Claire
- Kloft, Marius
- Müller, Klaus-Robert
- Görnitz, Nico
- Technische Universität Berlin
Time of origin
- 2020
Other Objects (12)
Spärlich verbundene neuronale Netze und ihre Anwendung
Efficient learning machines : from kernel methods to deep learning
On the extraction, classification and causal analysis of EEG signal components
Probabilistic Methods for Network Security. From Analysis to Response
Novel machine learning methods for computational chemistry
Optimizing Spatial Filters to reduce BCI Inefficiency
Advances in Neurotechnology for Brain Computer Interfaces
Towards EEG source connectivity analysis
Stationary Subspace Analysis: Towards understanding non-stationary data
Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)
Bag of Machine Learning Concepts for Visual Concept Recognition in Images
Identifying Interactions from Superimposed Signals
Spärlich verbundene neuronale Netze und ihre Anwendung
Efficient learning machines : from kernel methods to deep learning
On the extraction, classification and causal analysis of EEG signal components
Probabilistic Methods for Network Security. From Analysis to Response
Novel machine learning methods for computational chemistry
Optimizing Spatial Filters to reduce BCI Inefficiency
Advances in Neurotechnology for Brain Computer Interfaces
Towards EEG source connectivity analysis
Stationary Subspace Analysis: Towards understanding non-stationary data
Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)
Bag of Machine Learning Concepts for Visual Concept Recognition in Images
Identifying Interactions from Superimposed Signals
Spärlich verbundene neuronale Netze und ihre Anwendung
Efficient learning machines : from kernel methods to deep learning
On the extraction, classification and causal analysis of EEG signal components
Probabilistic Methods for Network Security. From Analysis to Response
Novel machine learning methods for computational chemistry
Optimizing Spatial Filters to reduce BCI Inefficiency
Advances in Neurotechnology for Brain Computer Interfaces
Towards EEG source connectivity analysis
Stationary Subspace Analysis: Towards understanding non-stationary data
Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)
Bag of Machine Learning Concepts for Visual Concept Recognition in Images