Development of a preliminary version of a model for machine learning in predicting yield on the example of wheat in the conditions of East Kazakhstan

2021 ◽  
Author(s):  
Nail Alikuly Beisekenov ◽  
Marzhan Anuarbekovna Sadenova ◽  
Natalya Anatolyevna Kulenova ◽  
Mamysheva Asel Mukhtarkanovna
Keyword(s):  
Machine Learning ◽  
East Kazakhstan ◽  
Preliminary Version

scholarly journals Determining Research Priorities for Astronomy Using Machine Learning

2022 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Brian Thomas ◽  
Harley Thronson ◽  
Anthony Buonomo ◽  
Louis Barbier
Keyword(s):  
Machine Learning ◽  
Latent Dirichlet Allocation ◽  
Research Priorities ◽  
Machine Learning Techniques ◽  
Annual Growth Rate ◽  
Compound Annual Growth Rate ◽  
Research Areas ◽  
Learning Techniques ◽  
Scientific Papers ◽  
Preliminary Version

Abstract We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high-impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey. One metric is based on a sum of the fractional contribution to each topic by all scientific papers (“counts”) while the other is the Compound Annual Growth Rate of counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning

The Hierarchical Model of Exercise Dependence

2015 ◽  
Vol 36 (4) ◽  
pp. 247-257 ◽  
Author(s):  
Gayatri Kotbagi ◽  
Laurence Kern ◽  
Lucia Romo ◽  
Ramesh Pathare
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Physical Exercise ◽  
Exploratory Factor Analysis ◽  
Convergent Validity ◽  
Large Population ◽  
Assessment Tools ◽  
Negative Consequences ◽  
Confirmatory Factor ◽  
Preliminary Version

Abstract. Physical exercise when done excessively may have negative consequences on physical and psychological wellbeing. There exist many scales to measure this phenomenon. The purpose of this article is to create a scale measuring the problematic practice of physical exercise (PPPE Scale) by combining two assessment tools already existing in the field of exercise dependency but anchored in different approaches (EDS-R and EDQ). This research consists of three studies carried out on three independent sample populations. The first study (N = 341) tested the construct validity (exploratory factor analysis); the second study (N = 195) tested the structural validity (confirmatory factor analysis) and the third study (N = 104) tested the convergent validity (correlations) of the preliminary version of the PPPE scale. Exploratory factor analysis identified six distinct dimensions associated with exercise dependency. Furthermore, confirmatory factor analysis validated a second order model consisting of 25 items with six dimensions and four sub-dimensions. The convergent validity of this scale with other constructs (GLTEQ, EAT26, and The Big Five Inventory [BFI]) is satisfactory. The preliminary version of the PPPE must be administered to a large population to refine its psychometric properties and develop scoring norms.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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