## 6 Parameters Initialization
It can be observed in Figure 4 that the images within each class have significant shape variations, setting a challenge for classification approaches. The size of each class varied from 31 to 800, and 4096-dimensional feature vectors were extracted from the data by training the 16-layer deep convolutional neural networks for large-scale visual recognition [56]. Figure 3: Face images for one subject from the AR dataset. Figure 2: Face images for one subject from the YaleB dataset. ### Fifteen Scene Category Database

The Fifteen Scene Category database [57] involves fifteen natural scene categories, and each image has an average size of 250 \(\times\) 300 pixels. The scenes include images from kitchens, living rooms, countrysides, etc. The number of samples for each category varies from 200 to 400. Figure 5 shows the images of eight of the fourteen categories. We used 3000-dimensional spatial pyramid features of the samples provided by [8]. ### Sports Actions Database

We used action bank features (processed data) [58] of the UCF sports action database [59] for action recognition. The database consists of 150 clips @10\(fps\) taken for 10 classes of varied sports actions. The clips contain the sports actions that include kicking, golfing, diving, horse riding, skateboarding, running, swinging, swinging highbar, lifting, and walking. Figure 6 shows some of the action examples. Our experiment consists of five five-fold group-wise cross-validations with different seed values for each cross-validation. Four folds are used in training, and the remaining one is used for testing. Figure 4: Images from three classes of the Catech-101 dataset. Figure 5: Fifteen scene images; images from eight categories. ## 8 Performance Measures

We use the following performance metrics to evaluate the performance of our approach to establish a comparison with previous approaches. These metrics are explained below. 1. **Classification accuracy:** Classification accuracy is defined as \[Acc.(\%)=(\frac{N_{t}}{N})100,\] (22) here, \(N_{t}\) and \(N\) are the number of test examples truly classified and the total number of test examples. We report the average accuracy, averaged over a number of experiments. 2. **Standard deviation:** We also evaluate our approach based on the standard deviation for an assessment of the error margins of the results. The standard deviation of the accuracy is measured as below. \[\sigma=\sqrt{\frac{\sum{(X-\mu)^{2}}}{N}}\] (23) Here, \(\sigma\), \(X\), \(\mu\), and \(N\) represent the standard deviation, each value of the parameter, the mean value of the parameter, and the total number of experiments. 3. **Confidence interval:** We also measure confidence intervals based upon the confidence level of 95% for the classification accuracies. The details for finding confidence intervals based on a 95% confidence level are given in Section 10. 4. **AUC-ROC score:** The ROC (receiver operating characteristic) curve of a class is drawn based upon One-vs-the-Rest (OvR) settings, considering the class as positive and all others as negative. The curve shows the trend of true positive rate with false positive rate for different threshold values for the positive-negative settings. The smaller values of false positive rates and larger values of true positive rates favor the performance of the model for classifying the class. A higher AUC (area under the curve) value of the ROC of a class shows the good performance of the classifier in distinguishing between true and false predictions. The average area under the ROC curves of all the positive-negative combinations (all classes) determines the performance of the classification model. This value is called the AUC-ROC score. 5. **Training time:** The total time taken by an experiment during training of a model determines the computational efficiency of the model. 6. **Test time of prediction of a test example:** This time also shows the efficiency of the model in predicting a test example.