Journal ArticleDOI
Pattern Recognition and Machine Learning
TLDR
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
Citations
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Journal ArticleDOI
The effects of day-to-day variability of physiological data on operator functional state classification.
TL;DR: The application of three popular pattern classification techniques to EEG data obtained from asymptotically trained subjects performing a complex multitask across five days in one month demonstrates that with proper methods, pattern classification is stable enough across days and weeks to be a valid, useful approach.
Journal ArticleDOI
Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images
TL;DR: A novel hierarchical CD approach is proposed, aimed at identifying all the possible change classes present between the considered images, by considering spectral change information to identify the change classes having discriminable spectral behaviors.
Posted ContentDOI
3D Variability Analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM
Ali Punjani,David J. Fleet +1 more
TL;DR: 3D variability analysis (3DVA) is introduced, an algorithm that fits a linear subspace model of conformational change to cryo-EM data at high resolution, enabling the resolution and visualization of detailed molecular motions of both large and small proteins.
Journal ArticleDOI
Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification
TL;DR: This paper presents the formulation of CM-KLOGR and its effectiveness through experiments that comparatively evaluated CM- KLOGR using benchmark imbalanced datasets.
Journal ArticleDOI
Modular toolkit for Data Processing (MDP): a Python data processing framework
TL;DR: The modular toolkit for Data Processing is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures.