Regularization for Sparseness and Smoothness : Applications in System Identification and Signal Processing
In system identification, the Akaike Information Criterion (AIC) is a well known method to balance the model fit against model complexity. Regularization here acts as a price on model complexity. In statistics and machine learning, regularization has gained popularity due to modeling methods such as...
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| Format: | Doctoral Thesis |
| Language: | English |
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Linköpings universitet, Reglerteknik
2010
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60531 http://nbn-resolving.de/urn:isbn:978-91-7393-287-5 |