Results 201 to 210 of about 1,286,262 (236)

Machine and Statistical Learning

2017
Databases and big data are used for constructing models to have a better understanding of the data, or to make decisions. Machine and statistical learning offer tools for this purpose. In this chapter we review some of the methods in these areas that are of relevance in this book.
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Statistical Machine Learning for Researchers

2023
This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical
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Multinomial conjunctoid statistical learning machines

[1988] The 15th Annual International Symposium on Computer Architecture. Conference Proceedings, 1988
Multinomial Conjunctoids are supervised statistical modules that learn the relationships among binary events. The multinomial conjunctoid algorithm precludes the following problems that occur in existing feedforward multi-layered neural networks: (a) existing networks often cannot determine underlying neural architectures, for example how many hidden ...
Y. Takefuji, R. Jannarone, Y. B. Cho, T. Chen   +3 more
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Challenges in Statistical Machine Learning

2005
Machine learning and statistics are one and the same discipline, with different communities of researchers attacking essentially the same fundamental problems from different perspectives. In this note we briefly describe some current challenges in the fi eld of statistical machine learning that cut across the communities.
Lafferty, John D., Wasserman, Larry
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Reliability in Machine Learning

Philosophy Compass
Issues of reliability are claiming center‐stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts
Thomas Grote, Konstantin Genin, Emily Sullivan   +2 more
semanticscholar   +1 more source

Is K-fold cross validation the best model selection method for Machine Learning?

arXiv.org
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is ...
J. M. Górriz, F. Segovia, J. Ramírez, A. Ortiz, J. Suckling   +4 more
semanticscholar   +1 more source

Statistical (Machine) Learning

2022
Andrew F. Siegel, Michael R. Wagner
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Statistical thinking, machine learning

Journal of Clinical Epidemiology, 2019
Jiang, Bian, Iain, Buchan, Yi, Guo, Mattia, Prosperi   +3 more
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Statistical Issues in Machine Learning

2008
Recursive partitioning methods from machine learning are being widely applied in many scientific fields such as, e.g., genetics and bioinformatics. The present work is concerned with the two main problems that arise in recursive partitioning, instability and biased variable selection, from a statistical point of view.
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Some Universal Insights on Divergences for Statistics, Machine Learning and Artificial Intelligence

Geometric Structures of Information, 2018
M. Broniatowski, W. Stummer
semanticscholar   +1 more source