Statistical learning models and individual differences.

1966 ◽  
Vol 73 (4) ◽  
pp. 357-364 ◽  
Author(s):  
R. A. Weitzman
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Models

An Exploration of Individual Differences in Statistical Learning

2014 ◽  
Author(s):  
Lucy C. Erickson ◽  
Michael Kaschak ◽  
Erik D. Thiessen ◽  
Cassie Berry
Keyword(s):  
Individual Differences ◽  
Statistical Learning

scholarly journals A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions

2021 ◽  
Vol 5 ◽  
pp. 100030
Author(s):  
Louis Ehwerhemuepha ◽  
Sidy Danioko ◽  
Shiva Verma ◽  
Rachel Marano ◽  
William Feaster ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Learning Models ◽  
Super Learner

Statistical Learning Models

1963 ◽  
Vol 33 (5) ◽  
pp. 543
Author(s):  
Ronald A. Weitzman
Keyword(s):  
Statistical Learning ◽  
Learning Models

scholarly journals Information-theoretic Modeling of Perceived Musical Complexity

2019 ◽  
Vol 37 (2) ◽  
pp. 165-178
Author(s):  
Sarah A. Sauvé ◽  
Marcus T. Pearce
Keyword(s):  
Individual Differences ◽  
Computational Model ◽  
Information Content ◽  
Statistical Learning ◽  
Probabilistic Prediction ◽  
Information Theoretic ◽  
Musical Background ◽  
Musical Excerpts ◽  
Definition Of ◽  
Perceived Complexity

What makes a piece of music appear complex to a listener? This research extends previous work by Eerola (2016), examining information content generated by a computational model of auditory expectation (IDyOM) based on statistical learning and probabilistic prediction as an empirical definition of perceived musical complexity. We systematically manipulated the melody, rhythm, and harmony of short polyphonic musical excerpts using the model to ensure that these manipulations systematically varied information content in the intended direction. Complexity ratings collected from 28 participants were found to positively correlate most strongly with melodic and harmonic information content, which corresponded to descriptive musical features such as the proportion of out-of-key notes and tonal ambiguity. When individual differences were considered, these explained more variance than the manipulated predictors. Musical background was not a significant predictor of complexity ratings. The results support information content, as implemented by IDyOM, as an information-theoretic measure of complexity as well as extending IDyOM's range of applications to perceived complexity.

Chapter VIII: Statistical Learning Models

1963 ◽  
Vol 33 (5) ◽  
pp. 543-555
Author(s):  
Ronald A. Weitzman
Keyword(s):  
Statistical Learning ◽  
Learning Models

Forecasting northern polar stratospheric variability with competing statistical learning models

2017 ◽  
Vol 143 (705) ◽  
pp. 1816-1827 ◽  
Author(s):  
Ivan Minokhin ◽  
Christopher G. Fletcher ◽  
Alexander Brenning
Keyword(s):  
Statistical Learning ◽  
Learning Models ◽  
Stratospheric Variability
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