Journal ArticleDOI
Pattern Recognition and Machine Learning
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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
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images.
Ezequiel Geremia,Olivier Clatz,Bjoern H. Menze,Ender Konukoglu,Antonio Criminisi,Nicholas Ayache +5 more
TL;DR: In an a posteriori analysis, it is shown how selected features during classification can be ranked according to their discriminative power and reveal the most important ones.
Journal ArticleDOI
A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
TL;DR: A short introduction into machine learning is given, its potential for precision crop protection is analyzed and an overview of instructive examples from different fields of precision agriculture is provided.
Proceedings ArticleDOI
Hypergraph spectral learning for multi-label classification
Liang Sun,Shuiwang Ji,Jieping Ye +2 more
TL;DR: This paper proposes an approximate formulation of a hypergraph spectral learning formulation for multi-label classification, which is shown to be equivalent to a least squares problem under a mild condition and indicates that the approximate formulation is much more efficient than the original one, while keeping competitive classification performance.
Journal ArticleDOI
Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization
TL;DR: A three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies and a principled manner of such selection is proposed, with very promising results (well over 90% accuracies) and robustness are observed on all three‐ level deep learning models.
Posted Content
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
TL;DR: It is demonstrated that TreeNets can improve ensemble performance and that diverse ensembles can be trained end-to-end under a unified loss, achieving significantly higher "oracle" accuracies than classical ensembled.