(a) The run time as a function of spike train length using Knight’s method (black), our method (red), and the standard MATLAB method (green) for a sparseness of 5%. N = 10 and error bars are standard deviation. (b) The run time as a function of spike train length for different sparseness values: dotted line (1%), dashed line (5%), solid line (25%). N = 100, error bars are standard deviation, and colors are the same as in (a).</p
<p>Computation time (in seconds) for several implementations of the evaluated clustering algorithms ...
<p>Computational time is depicted as a function of genome size when setting the sample size to 50, a...
We show results for the following networks: WikiVote; NetHEPT; Epinions; Email-EuAll. Each plot depi...
<p>Comparison of running time (seconds) of the algorithms implemented in MATLAB (upper section) and ...
Top row corresponds to the run-times in seconds of different methods in scenario (S1) and scenario (...
The comparison of the average running time of the examined methods for 150 experiments executed for ...
<p>Solid, dotted and dashed lines represents the averaged computation times required for solving the...
<p>A) Relationship between the number of data points and computational time for the different period...
<p>Comparison of our approach and counterpart algorithms in terms of running time (<i>s</i>).</p
<p>Running times and prediction accuracies of the sub-quadratic algorithm tested with datasets of di...
Computational time comparison of five methods on blocks drawn from six data sets (seconds).</p
<p>Computation time (in seconds) for several implementations of the evaluated clustering algorithms ...
Fig 4 shows the execution time for computing approximation values and performing simulation runs. α ...
<p>Each point is the average of 10 runs, with the error bars denoting the standard error on the mean...
<p>Approximation of the average running time before finding the true pattern.</p
<p>Computation time (in seconds) for several implementations of the evaluated clustering algorithms ...
<p>Computational time is depicted as a function of genome size when setting the sample size to 50, a...
We show results for the following networks: WikiVote; NetHEPT; Epinions; Email-EuAll. Each plot depi...
<p>Comparison of running time (seconds) of the algorithms implemented in MATLAB (upper section) and ...
Top row corresponds to the run-times in seconds of different methods in scenario (S1) and scenario (...
The comparison of the average running time of the examined methods for 150 experiments executed for ...
<p>Solid, dotted and dashed lines represents the averaged computation times required for solving the...
<p>A) Relationship between the number of data points and computational time for the different period...
<p>Comparison of our approach and counterpart algorithms in terms of running time (<i>s</i>).</p
<p>Running times and prediction accuracies of the sub-quadratic algorithm tested with datasets of di...
Computational time comparison of five methods on blocks drawn from six data sets (seconds).</p
<p>Computation time (in seconds) for several implementations of the evaluated clustering algorithms ...
Fig 4 shows the execution time for computing approximation values and performing simulation runs. α ...
<p>Each point is the average of 10 runs, with the error bars denoting the standard error on the mean...
<p>Approximation of the average running time before finding the true pattern.</p
<p>Computation time (in seconds) for several implementations of the evaluated clustering algorithms ...
<p>Computational time is depicted as a function of genome size when setting the sample size to 50, a...
We show results for the following networks: WikiVote; NetHEPT; Epinions; Email-EuAll. Each plot depi...