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
More filters
Journal ArticleDOI
Fully Automated Versus Standard Tracking of Left Ventricular Ejection Fraction and Longitudinal Strain: The FAST-EFs Multicenter Study.
Christian Knackstedt,Sebastiaan C.A.M. Bekkers,Georg Schummers,Marcus Schreckenberg,Denisa Muraru,Luigi P. Badano,Andreas Franke,Chirag Bavishi,Alaa Mabrouk Salem Omar,Partho P. Sengupta +9 more
TL;DR: Fully automated analysis of echocardiography images provides rapid and reproducible assessment of left ventricular EF and LS.
Journal ArticleDOI
Machine learning pipeline for battery state-of-health estimation
Darius Roman,Saurabh Saxena,Saurabh Saxena,Valentin Robu,Valentin Robu,Michael Pecht,David Flynn +6 more
TL;DR: This work designs and evaluates a machine learning pipeline for estimation of battery capacity fade—a metric of battery health—on 179 cells cycled under various conditions, and provides insights into the design of scalable data-driven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction.
Journal ArticleDOI
Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems
TL;DR: The design of a class of machine-learning models, namely neural networks, for the load forecasts of medium-voltage/low-voltages substations are described and the results show that the neural network-based models outperform the time series models.
Journal ArticleDOI
Coupled Gaussian processes for pose-invariant facial expression recognition
TL;DR: The proposed Coupled Scaled Gaussian Process Regression model for head-posing normalization outperforms state-of-the-art regression-based approaches to head-pose normalization, 2D and 3D Point Distribution Models (PDMs), and Active Appearance Models (AAMs), especially in cases of unknown poses and imbalanced training data.
Journal ArticleDOI
Inference in the age of big data: Future perspectives on neuroscience.
Danilo Bzdok,B.T. Thomas Yeo +1 more
TL;DR: It is argued that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.