Journal ArticleDOI
Pattern Recognition and Machine Learning
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This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
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Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values
TL;DR: This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information and constructed prediction models for breast cancer survivability using K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines.
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Travel cost inference from sparse, spatio temporally correlated time series using Markov models
TL;DR: This work uses spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series to predict travel cost from GPS tracking data from probe vehicles, and provides algorithms that are able to learn the parameters of an STHMM while contending with the sparsity, spatiotemporal correlation, and heterogeneity of the time series.
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Unobtrusive Assessment of Students' Emotional Engagement during Lectures Using Electrodermal Activity Sensors
TL;DR: This paper shows that off-the-shelf wearable devices can be used to unobtrusively monitor the emotional engagement of students during lectures and proposes the use of several novel features to capture students' momentary engagement.
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Multicontact Locomotion of Legged Robots
TL;DR: This paper proposes a complete solution relying on a generic template model, based on the centroidal dynamics, able to quickly compute multicontact locomotion trajectories for any legged robot on arbitrary terrains, and is thus not limited by arbitrary assumption.
Journal ArticleDOI
Spinal cord grey matter segmentation challenge.
Ferran Prados,Ferran Prados,John Ashburner,Claudia Blaiotta,Tom Brosch,Julio Carballido-Gamio,Manuel Jorge Cardoso,Benjamin N. Conrad,Esha Datta,Gergely David,Benjamin De Leener,Sara M. Dupont,Patrick Freund,Claudia A. M. Wheeler-Kingshott,Claudia A. M. Wheeler-Kingshott,Francesco Grussu,Roland G. Henry,Bennett A. Landman,Emil Ljungberg,Bailey Lyttle,Sebastien Ourselin,Nico Papinutto,Salvatore Saporito,Regina Schlaeger,Seth A. Smith,Paul Summers,Roger Tam,Marios C. Yiannakas,Alyssa H. Zhu,Julien Cohen-Adad,Julien Cohen-Adad +30 more
TL;DR: Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold‐standard, to characterize the state‐of‐the‐art in the field as well as identifying new opportunities for future improvements.