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algorithm from » algorithm flow (Expand Search)
from function » from functional (Expand Search), fc function (Expand Search)
algorithm b » algorithm _ (Expand Search), algorithms _ (Expand Search)
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Results of the application of different clustering algorithms to average functional connectivity from healthy subjects.
Published 2023“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”
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Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.
Published 2024“…<p>(A), (B) Algorithm performance, evaluated over 50 simulated datasets generated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.g001" target="_blank">Fig 1</a> with <i>N</i> = 3 true groups, 900 samples and 10% simulated measurement noise. …”
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Algorithm membership function.
Published 2022“…The x-axis plots blood glucose values and the y-axis plots the range of truth value from 0 to 1. (Bottom) Output Membership Function involved in the center of gravity defuzzification process to produce basal-rate insulin changes.…”
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Example performance of the smooth-index algorithm.
Published 2024“…<b>D</b> Different cost functions as the algorithm performs the gradient descent. …”
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Signal detection algorithm adapted from [1] yields exponential distributions and unrealistic mean durations of percepts.
Published 2020“…(Bottom) Trial-by-trial applications of the signal detection algorithm from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>] with A: <i>C</i><sub><i>th</i></sub> = 4.01 and B: <i>C</i><sub><i>th</i></sub> = 4.21 yield exponentially distributed subsequent percept durations for <i>I</i> (blue) and <i>S</i> (red). …”
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The signal detection algorithm for constructing a neurometric function (the probability of segregation as a function of time) generates acceptable buildup fits at <i>DF</i> = 1, 3, 6, 9.
Published 2020“…Lower panel: The signal detection algorithm constructs neurometric functions using numerical data from all <i>N</i><sub><i>in</i></sub> neuronal units. …”
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The optimal contact/quarantine rates from the family of functions (4) and (5) for Hainan and Xinjiang.
Published 2023“…(b, e) Root mean square error(), corresponding to fitting the time-dependent quarantine rate learned by TDINN algorithm using <i>q</i><sub>1</sub>(<i>t</i>), <i>q</i><sub>2</sub>(<i>t</i>) and <i>q</i><sub>3</sub>(<i>t</i>) in Hainan and Xinjiang. …”
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If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions.
Published 2024“…Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”
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Performance of the smooth-index algorithm for matrices with different densities.
Published 2024“…B</b> Performance of the algorithm as a function of the number of neurons. …”