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For 2001 MODIS time-series data, SVM generated overall accuracies ranging from 77% to 80% for training sample sizes from 20 to 800 pixels per class, compared to 67 76% and 62 73% for NN and CART, respectively.
Recall that the conventional GIP algorithm, which is used for training sample selection, has the following quadratic form [12, 13].
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defining thresholds for training samples.
The test sample size was fixed at 5000 for all tests when varying the size for training samples.
A stepwise execution of the following three stages characterises the workflow of this approach: 1) defining thresholds for training samples 2) evaluating AD for new/test samples 3) optimizing the smoothing parameter k .
Table 1 shows the comparisons of the face recognition accuracies of the proposed 2D DGHM-based method with that of the 2D-PCA [25], 2D-LDA [27], 2D-CCA [29], and 2D-KCM [43] methods for training samples of sizes 3 to 7. The average number of probe images for the databases decrease with the number of training images per subject.
About the image fusion, an information-based approach was proposed in article [7] for assessing the fused image quality by the use of a set of primitive indices which can be calculated automatically without a requirement for training samples or machine learning.
simple SVMs classifier was trained through binary encoding for training samples and built with RBF kernel in LIBSVM package[11].
Second, the voxels selected for training samples for each tissue class were chosen randomly.
In the recursive addition of the features, for training samples, a classification model is one of the best methods.
With the given class labels for training samples, supervised distance metric learning can be divided into global distance metric learning and local distance metric learning.
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