TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Predictive modeling using sparse logistic regression with applications

Tutkimustuotos

Yksityiskohdat

Julkaisun otsikon käännösPredictive modeling using sparse logistic regression with applications
AlkuperäiskieliEnglanti
JulkaisupaikkaTampere
KustantajaTampere University of Technology
Sivumäärä97
ISBN (elektroninen)978-952-15-3233-7
ISBN (painettu)978-952-15-3226-9
TilaJulkaistu - 31 tammikuuta 2014
OKM-julkaisutyyppiG4 Monografiaväitöskirja

Julkaisusarja

NimiTampere University of Techology. Publication
KustantajaTampere University of Technology
Vuosikerta1190
ISSN (painettu)1459-2045

Tiivistelmä

In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data classification. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classification method such as logistic regression often outperforms more sophisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of L1 regularized parameter estimation have a significant role in this thesis. It is shown that a combination of a careful model assessment scheme and automatic feature selection by means of logistic regression model and coefficient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classification.

Julkaisufoorumi-taso

Latausten tilastot

Ei tietoja saatavilla