Learning in compressed space

DOI10.1016/J.NEUNET.2013.01.020zbMath1293.68221DBLPjournals/nn/FabischKWK13OpenAlexW2027138707WikidataQ45959503 ScholiaQ45959503MaRDI QIDQ2510741

Hendrik Wöhrle, Frank Kirchner, Yohannes Kassahun, Alexander Fabisch

Publication date: 4 August 2014

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2013.01.020

zbMATH Keywords

supervised learningartificial neural networksreinforcement learningcompressed sensingmodel compression

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• LIBSVM
• Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A simple proof of the restricted isometry property for random matrices
• Multilayer feedforward networks are universal approximators
• Extensions of Lipschitz mappings into a Hilbert space
• Learning Deep Architectures for AI
• Decoding by Linear Programming
• Neuroevolution strategies for episodic reinforcement learning
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• 10.1162/153244303322753616
• Learning representations by back-propagating errors
• A method for the solution of certain non-linear problems in least squares
• Compressed sensing

This page was built for publication: Learning in compressed space