For the Polo dataset, we select 10 sample training images from the training set to learn our object detector.
Apply Equation 20 to the sample training set in Figure 6.5 to estimate the best value of for this sample.
After collecting the fluorescent images of the soybean milk samples, the top seven wavelet moment invariants are selected according to the sample training and experimental comparison.
The sample training was implemented on an Instron E3000.
Four edge orientations were used in the sample training task: 0∘, 45∘, 90∘ and 135∘.
The amount of penalization was objectively determined by leave-one-out cross-validation within the sample training set.
CMS relies on simulated events to determine resolutions and trigger and reconstruction efficiencies, and to provide the signal sample for training the BDT.
At first, DWT is used to decompose the in-sample training dataset of the time series into linear (detailed) and non-linear (approximate) parts.
Briefly, LOOCV uses a single observation from the dataset for validation and all remaining samples for training the model.
As discussed in the section on overfitting, this motivates splitting the data into two random samples, training and test, and using the regions from the training sample to compute the definitive ROC curve based on data in the test sample.
