When the blind curve is finite: dimension estimation and model inference based on empirical waveforms.
about
Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal.Making sense of the noise: Replication difficulties of Correll's (2008) modulation of 1/f noise in a racial bias task.Fractal analyses: statistical and methodological innovations and best practices.A comment on "Measuring fractality" by Stadnitski (2012).Multifractal analyses of human response time: potential pitfalls in the interpretation of results.From the Role of Context to the Measurement Problem: The Dutch Connection Pays Tribute to Guy Van Orden
P2860
When the blind curve is finite: dimension estimation and model inference based on empirical waveforms.
description
2013 nî lūn-bûn
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2013年の論文
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2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
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2013年论文
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2013年论文
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name
When the blind curve is finite ...... based on empirical waveforms.
@en
When the blind curve is finite ...... based on empirical waveforms.
@nl
type
label
When the blind curve is finite ...... based on empirical waveforms.
@en
When the blind curve is finite ...... based on empirical waveforms.
@nl
prefLabel
When the blind curve is finite ...... based on empirical waveforms.
@en
When the blind curve is finite ...... based on empirical waveforms.
@nl
P2860
P356
P1476
When the blind curve is finite ...... based on empirical waveforms.
@en
P2860
P356
10.3389/FPHYS.2013.00075
P577
2013-04-08T00:00:00Z