Results 231 to 240 of about 874,982 (263)

Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis. [PDF]

PLoS Med
Spittal MJ, Guo XA, Kang L, Kirtley OJ, Clapperton A, Hawton K, Kapur N, Pirkis J, Carter G.   +8 more
europepmc   +1 more source

Bridging Traditional-Statistics and Machine-Learning Approaches in Psychology: Navigating Small Samples, Measurement Error, Nonindependent Observations, and Missing Data

Rosa Lavelle-Hill, Gavin Smith, Kou Murayama   +2 more
openalex   +1 more source

Multi-level QTAIM-enriched graph neural networks for resolving properties of transition metal complexes.

Digit Discov
Gee W, Doyle A, Vargas S, Alexandrova AN.   +3 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Statistics meets Machine Learning

Oberwolfach Reports, 2021
Theory 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,
Lutz Dümbgen, Klaus-Robert Müller, Richard J. Samworth, Fadoua Balabdaoui   +3 more
openaire   +2 more sources

Machine Learning and Statistics: The Interface

Journal of the American Statistical Association, 1998
Statistical Properties of Tree-Based Approaches to Classification The Decision Tree Algorithm CAL5 Based on a Statistical Approach to its Splitting Algorithm Probabilistic Symbolic Classifiers: An Empirical Comparison from a Statistical Perspective A Multistrategy Approach to Learning Multiple Dependent Concepts Quality of Decision Rules - Definition ...
Michael J. Turmon, Gholamreza Nakhaeizadeh, Charles C. Taylor   +2 more
openaire   +2 more sources

Machine learning versus statistical modeling

Biometrical Journal, 2014
This 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
Matthias Schmid, Anne-Laure Boulesteix
openaire   +3 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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 ...
Robert J. Jannarone, T. Chen, Yoshiyasu Takefuji, Yong B. Cho   +3 more
openaire   +2 more sources