Effects of pretraining and stimulus composition on rule learning.

1969 ◽  
Vol 80 (3, Pt.1) ◽  
pp. 450-454 ◽  
Author(s):  
Peter J. Johnson ◽  
Roger H. White
Keyword(s):  
Rule Learning

Probabilistic Rule Learning Systems

2021 ◽  
Vol 54 (4) ◽  
pp. 1-16
Author(s):  
Abdus Salam ◽  
Rolf Schwitter ◽  
Mehmet A. Orgun
Keyword(s):  
Probability Theory ◽  
Rule Learning ◽  
Background Knowledge ◽  
Learning Systems ◽  
Future Research ◽  
Research Directions ◽  
System Architectures ◽  
Probabilistic Rule ◽  
Future Research Directions ◽  
High Level

This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.

Rule learning transfer across linguistic and visual modalities in 7‐month‐old infants

Infancy ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 442-454
Author(s):  
Hermann Bulf ◽  
Ermanno Quadrelli ◽  
Shannon Brady ◽  
Bryan Nguyen ◽  
Viola Macchi Cassia ◽  
...  
Keyword(s):  
Learning Transfer ◽  
Rule Learning

scholarly journals Intra-language: the study of L2 morpheme productivity as within-item variance

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Stefano Rastelli
Keyword(s):  
Rule Learning ◽  
Rote Learning ◽  
Association Measure ◽  
Grammatical Competence ◽  
Type Frequency

Abstract This article suggests a method to appraise L2 morphemes productivity in longitudinal learner data. Traditionally, morpheme productivity is believed to depend on type frequency and on proportion between inflected and uninflected lexemes. However, such measures cannot distinguish between rote-learning and rule-learning of target-like forms. In contrast, the association measure ΔP (delta pi) can quantify the extent to which a morpheme is contingent upon a limited number of lexemes. Decreasing contingency might parallel learners’ increasing awareness of asymmetrical morpheme-lexeme distribution in the input and this might be a cue of developing L2 grammatical competence beyond appearances. The paper presents the rationale and procedure for analyzing within-item variance – or the ‘intra-language’ – and illustrates a case-study concerning the perfective morpheme in L2 Italian.

scholarly journals JADE: A tool for medical researchers to explore adverse drug events using health claims data

2014 ◽  
Vol 05 (03) ◽  
pp. 621-629 ◽  
Author(s):  
S.K. Sauter ◽  
C. Rinner ◽  
L.M. Neuhofer ◽  
M. Wolzt ◽  
W. Grossmann ◽  
...  
Keyword(s):  
Adverse Drug Events ◽  
Claims Data ◽  
Age Groups ◽  
Rule Learning ◽  
Health Claims ◽  
Drug Event ◽  
Study Cohort ◽  
Defined Daily Doses ◽  
Health Claims Data ◽  
Medical Researchers

SummaryObjective: The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays.Methods: We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database.Results: Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented.Conclusion: The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.Citation: Edlinger D, Sauter SK, Rinner C, Neuhofer LM, Wolzt M, Grossmann W, Endel G, Gall W. JADE: A tool for medical researchers to explore adverse drug events using health claims data. Appl Clin Inf 2014; 5: 621–629http://dx.doi.org/10.4338/ACI-2014-04-RA-0036

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