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Statistics meets Machine Learning
Oberwolfach Reports, 2021Theory and application go hand in hand in most areas of statistics. In a world flooded with huge amounts of data waiting to be analyzed, classified and transformed into useful outputs, the designing of fast, robust and stable algorithms has never been as important as it is today. On the other hand, irrespective of whether the focus is put on estimation,
Fadoua Balabdaoui +3 more
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Machine learning versus statistical modeling
Biometrical Journal, 2014This is a discussion of the following papers: “Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory” by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and “Probability estimation with machine learning methods for dichotomous and multicategory
Boulesteix, Anne-Laure, Schmid, Matthias
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2019
AbstractThis chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way which demonstrates how these techniques can be viewed as special cases of a more general probabilistic model which we fit to some data.
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AbstractThis chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way which demonstrates how these techniques can be viewed as special cases of a more general probabilistic model which we fit to some data.
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Machine and Statistical Learning
2017Databases 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
2023This 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, 1988Multinomial 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 +3 more
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