These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]
These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 45 traces are TP in the first run, 40 in the second, and 42 in the third, then the value 40 is used as aggregated … [Read more…]
These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 10 traces are TP in the first run, 0 in the second, and 9 in the third, then the value 8 is used as aggregated … [Read more…]
These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 10 traces are TP in the first run, 8 in the second, and 9 in the third, then the value 8 is used as aggregated … [Read more…]
These graphs show the aggregated classification over three different runs, where for every category (FN, TP, TN, FP) the minimal number over the different runs is taken. For example, if 500 traces are TP in the first run, 490 in the second, and 495 in the third, then the value 490 is used as aggregated … [Read more…]
This graph shows the average accuracy values over three different runs for different categories of models. This graph shows the average accuracy values over three different runs for the situation without noise and with noise. For example, it shows that if there is no noise accuracy may be 100%, but adding noise results in a … [Read more…]
This graph shows the average accuracy values over three different runs for different models. This graph shows the average accuracy values over three different runs for different categories of models.
This graph shows the average accuracy values over three different runs for different categories of models.
This graph shows the average accuracy values over three different runs for different categories of models.
This graph shows the average accuracy values over three different runs for different categories of models. This graph shows the average accuracy values over three different runs for the situation without noise (Z=00) and with noise (Z≠00). For example, it shows that if there is no noise accuracy is close to 100%, but adding noise … [Read more…]