Book ChapterDOI
Learning from Data
Mehmed Kantardzic
- pp 87-139
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This chapter contains sections titled: Learning Machine Statistical Learning Theory Types of Learning Methods Common Learning Tasks Model Estimation Review Questions and Problems References for further study.Abstract:
This chapter contains sections titled: Learning Machine Statistical Learning Theory Types of Learning Methods Common Learning Tasks Model Estimation Review Questions and Problems References for further studyread more
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Machine Learning in Medicine.
TL;DR: What obstacles there may be to changing the practice of medicine through statistical learning approaches, and how these might be overcome are identified.
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Ten quick tips for machine learning in computational biology
TL;DR: Ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that the authors observed hundreds of times in multiple bioinformatics projects are presented.
Posted Content
Structured Pruning of Deep Convolutional Neural Networks
TL;DR: In this article, the importance weight of each particle is assigned by computing the misclassification rate with corresponding connectivity pattern, and the pruned network is re-trained to compensate for the losses due to pruning.
Journal ArticleDOI
High-Entropy Alloys as a Discovery Platform for Electrocatalysis
Thomas A. A. Batchelor,Jack K. Pedersen,Simon H. Winther,Ivano E. Castelli,Karsten Wedel Jacobsen,Jan Rossmeisl +5 more
TL;DR: In this article, density functional theory (DFT) was used to predict the remaining adsorption energies on a random subset of available binding sites on the surface of the HEA IrPdPtRhRu.
Journal ArticleDOI
DREAM: diabetic retinopathy analysis using machine learning.
TL;DR: A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step and the bright lesions areclassified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms.
References
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Journal ArticleDOI
Machine Learning in Medicine.
TL;DR: What obstacles there may be to changing the practice of medicine through statistical learning approaches, and how these might be overcome are identified.
Journal ArticleDOI
Ten quick tips for machine learning in computational biology
TL;DR: Ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that the authors observed hundreds of times in multiple bioinformatics projects are presented.
Journal ArticleDOI
Structured Pruning of Deep Convolutional Neural Networks
TL;DR: The proposed work shows that when pruning granularities are applied in combination, the CIFAR-10 network can be pruned by more than 70% with less than a 1% loss in accuracy.
Journal ArticleDOI
High-Entropy Alloys as a Discovery Platform for Electrocatalysis
Thomas A. A. Batchelor,Jack K. Pedersen,Simon H. Winther,Ivano E. Castelli,Karsten Wedel Jacobsen,Jan Rossmeisl +5 more
TL;DR: In this article, density functional theory (DFT) was used to predict the remaining adsorption energies on a random subset of available binding sites on the surface of the HEA IrPdPtRhRu.
Journal ArticleDOI
DREAM: diabetic retinopathy analysis using machine learning.
TL;DR: A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step and the bright lesions areclassified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms.