Results 221 to 230 of about 1,306 (257)
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Solutions of problems in analytic and extremal combinatorial set theory
2012Dottorato di Ricerca in Matematica ed Informatica, Ciclo XXIV SSD MAT/052011 ...
Nardi, Caterina +3 more
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Extremely Difficult Negotiator Goals: Do They Follow the Predictions of Goal-Setting Theory?
Academy of Management Proceedings, 2012Traditional goal-setting theory has been applied extensively in negotiation research. We examine one of the major tenets of the theory that has yet to be tested in the negotiation context, the argu...
Edward W. Miles, Elizabeth F. Clenney
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An Easy Reduction of an Isoperimetric Inequality on the Sphere to Extremal Set Theory
The American Mathematical Monthly, 2000In ancient times the extent of a city or an armed camp was often given in terms of its perimeter (so that a town would be described as requiring so many thousand paces to walk round). In the same way, according to Proclus, certain socialistic communities used to divide land so that each family received a plot of equal perimeter and it may have been in ...
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Kernel extreme learning machine based on fuzzy set theory for multi-label classification
International Journal of Machine Learning and Cybernetics, 2017Multi-label classification is a special kind of classification problem, where a single instance can be labeled to more than one class. Extreme learning machine (ELM) with kernel is an efficient method for solving both regression and multi-class classification problems.
Yanika Kongsorot +3 more
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Applying Organisational Theory to Isolated, Confined and Extreme Settings
The Australian and New Zealand Journal of Organisational Psychology, 2008AbstractResearch on person–environment fit theory has largely developed within the context of people and organisations in urban settings. There has been little research of this kind within organisations in isolated and confined contexts. The purpose of this article was to examine the implications of person–environment fit theory within the context of ...
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International Journal of Industrial Ergonomics, 1994
Abstract Cumulative Trauma Disorders (CTDs) of the upper extremity are physical ailments of the wrist, arm, and shoulder, caused by the cumulative effect of repeated mechanical stresses. They develop gradually over a period of time and are often believed to be work-related.
Thomas Wayne Merritt +1 more
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Abstract Cumulative Trauma Disorders (CTDs) of the upper extremity are physical ailments of the wrist, arm, and shoulder, caused by the cumulative effect of repeated mechanical stresses. They develop gradually over a period of time and are often believed to be work-related.
Thomas Wayne Merritt +1 more
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Cognitive Computation, 2016
Recently, a simple and efficient learning algorithm for single hidden layer feedforward networks (SLFNs) called extreme learning machine (ELM) has been developed by G.-B. Huang et al. One key strength of ELM algorithm is that there is only one parameter, the number of hidden nodes, to be determined while it has the significantly low computational time ...
Li Xu +3 more
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Recently, a simple and efficient learning algorithm for single hidden layer feedforward networks (SLFNs) called extreme learning machine (ELM) has been developed by G.-B. Huang et al. One key strength of ELM algorithm is that there is only one parameter, the number of hidden nodes, to be determined while it has the significantly low computational time ...
Li Xu +3 more
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Open Set Radar HRRP Recognition Based on Random Forest and Extreme Value Theory
2018 International Conference on Radar (RADAR), 2018Most of the progresses achieved in radar high range resolution profile (HRRP) recognition rely on the closed set condition, where the test sample is from a known class. In realistic scenario, however, the test sample may be drawn from unknown classes, which is regarded as an open set recognition task.
Yanhua Wang +4 more
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Mathematische Zeitschrift, 2018
Let \(I\) be a measurable subset of \([0,1]\) (the Lebesgue measure \(\mu\) are used), \(Q>1\) be a positive integer. The following class of polynomials is considered: \[ \mathcal P_n(Q)=\{P\in \mathcal P_n: H(P)\le Q\}, \] where \(H=H(P)\) is the height of any polynomial \[ P=a_nx^n+a_{n-1}x^{n-1}+\dots+a_1x+a_0\in\mathcal P_n, \] i.e., the height is ...
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Let \(I\) be a measurable subset of \([0,1]\) (the Lebesgue measure \(\mu\) are used), \(Q>1\) be a positive integer. The following class of polynomials is considered: \[ \mathcal P_n(Q)=\{P\in \mathcal P_n: H(P)\le Q\}, \] where \(H=H(P)\) is the height of any polynomial \[ P=a_nx^n+a_{n-1}x^{n-1}+\dots+a_1x+a_0\in\mathcal P_n, \] i.e., the height is ...
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A remark on extremal sets in the theory of polynomial interpolation
Studia Scientiarum Mathematicarum Hungarica, 1974Let the numbers x1, x2, ..., x~ (n 2) be prescribed on the interval [ ~, 1] with — I ~ x1 <x~ ~ ... ~ x,~ ~ 1. Denoting the fundamental Lagrange interpolating polynomials by 4(x), we introduce the Lebesgue function )~~(x) ~ 1(x). The question arises as how to choose the nodes x1, x~,..., x~ in such a way that max %~(x) will be as small as possible. (
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