Results 71 to 80 of about 2,045,405 (349)
Statistics and Machine Learning Experiments in Poetry
Sci, 2020 This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, information energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author.
openaire +2 more sources
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Background The use of deep learning‐based auto‐contouring algorithms in various treatment planning services is increasingly common. There is a notable deficit of commercially or publicly available models trained on large or diverse datasets containing high‐dose‐rate (HDR) brachytherapy treatment scans, leading to poor performance on images ...
Andrew J. Krupien +8 more
wiley +1 more source
STATSREP-ML: Statistical Evaluation & Reporting Framework for Machine Learning Results [PDF]
, 2014 In this report, we present STATSREP-ML, which is an open-source solution for automating the process of evaluating machine-learning results. It calculates qualitative statistics, performs the appropriate tests and reports them in a comprehensive way.
Guckelsberger, Christian, Schulz, Axel
core
Causality and Statistical Learning [PDF]
arXiv, 2010 We review some approaches and philosophies of causal inference coming from sociology, economics, computer science, cognitive science, and ...
arxiv
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose Volumetric‐modulated arc therapy (VMAT) treatment planning allows a compromise between a sufficient coverage of the planning target volume (PTV) and a simultaneous sparing of organs‐at‐risk (OARs). Particularly in the case of lung tumors, deciding whether it is possible or worth spending more time on further improvements of a treatment
Johann Brand +4 more
wiley +1 more source
A Comparative Study of Statistical Learning and Adaptive Learning [PDF]
arXiv, 2015 Numerous strategies have been adopted in order to make the process of learning simple, efficient and within less amount of time.. Classroom learning is slowly replaced by E-learning and M- learning. These techniques involve the usage of computers, smart phones and tablets for the process of learning.
arxiv
Learning Optimal Test Statistics in the Presence of Nuisance Parameters [PDF]
arXiv, 2022 The design of optimal test statistics is a key task in frequentist statistics and for a number of scenarios optimal test statistics such as the profile-likelihood ratio are known. By turning this argument around we can find the profile likelihood ratio even in likelihood-free cases, where only samples from a simulator are available, by optimizing a ...
arxiv
How Machines Learned to Think Statistically [PDF]
Significance, 2015 Abstract Creating artificial human intelligence has proved more difficult than first imagined. But thanks to statistical ideas and models, our machines are getting smarter. Brian Tarran reports An existential risk?
Zoubin Ghahramani, Brian Tarran
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Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC). Methods Postoperative PDAC patients were retrospectively selected, all of whom had undergone preoperative CT imaging and surgery. Both patients with resectable or borderline‐resectable pancreatic cancer met
Xinze Du +7 more
wiley +1 more source
An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure [PDF]
, 2013 We present an exploration of the rich theoretical connections between several classes of regularized models, network flows, and recent results in submodular function theory.
Koepke, Hoyt, Meila, Marina
core