T
Tapio Manninen
Researcher at Tampere University of Technology
Publications - 11
Citations - 197
Tapio Manninen is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Feature selection & Printed electronics. The author has an hindex of 5, co-authored 11 publications receiving 180 citations.
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Proceedings ArticleDOI
The 9th annual MLSP competition: New methods for acoustic classification of multiple simultaneous bird species in a noisy environment
Forrest Briggs,Yonghong Huang,Raviv Raich,Konstantinos Eftaxias,Zhong Lei,William Cukierski,Sarah Frey Hadley,Adam S. Hadley,Matthew G. Betts,Xiaoli Z. Fern,Jed Irvine,Lawrence Neal,Anil Thomas,Gabor Fodor,Grigorios Tsoumakas,Hong-Wei Ng,Thi Ngoc Tho Nguyen,Heikki Huttunen,Pekka Ruusuvuori,Tapio Manninen,Aleksandr Diment,Tuomas Virtanen,Julien Marzat,Joseph Defretin,David R. Callender,Chris Hurlburt,Ken Larrey,Maxim Milakov +27 more
TL;DR: It is an open problem for signal processing and machine learning to reliably identify bird sounds in real-world audio data collected in an acoustic monitoring scenario.
Journal ArticleDOI
Mind reading with regularized multinomial logistic regression
TL;DR: The proposed method, based on a regularized logistic regression model, is participated in the MEG mind reading competition of ICANN’11 conference, and was the best among ten submissions, reaching accuracy of 68 % of correct classifications in this five category problem.
Journal ArticleDOI
Leukemia prediction using sparse logistic regression.
TL;DR: A supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements using a data driven approach with machine learning methods to train a computational model that gives a confidence score of the patient being AML-positive.
Proceedings Article
Image segmentation using sparse logistic regression with spatial prior
TL;DR: The novelty of the approach stems from the use of a generic artificial feature set and the embedded feature selection property of the sparse logistic regression framework, which avoids application specific feature engineering.
Proceedings ArticleDOI
Bayesian error estimation and model selection in sparse logistic regression
TL;DR: The model selection by the new Bayesian error estimator is experimentally shown to improve the classification accuracy in small sample-size situations, and is able to avoid the excess variability inherent to traditional cross validation approaches.