# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 3 Experimental Results
### Comparison with Offline Learning Algorithms
#### 3.2.2 Caltech Multi-Class Classification  
In this section, we perform a comparison with other offline learning algorithms that use multiple features on the Caltech [36] database. This database provides a four-class classification problem-aircrafts, motorcycles, vehicles, and faces. There are 1074 aircrafts, 826 motorcycles, 1155 vehicles, and 450 faces. The negative samples used 900 background images for aircrafts, motorcycles, faces, and 1370 background images including roads and street scenes for vehicles.  
While using this database, we combined all the background images together and performed a comparison using the same standard as the comparison target [37], which used 150 random positive samples and 150 negative samples in each class, totaling 1200 positive and negative samples in four classes, to perform the training. The other positive and negative samples were for testing. We also performed eight experiments, and the final results are shown by the error rates, as indicated in Table 5. We found that the standard deviation was only 0.2%, which again proves the stability of our research.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Bikes** & **Vehicles** & **Motorcycles** & **Pedestrian** & **Average** \\ \hline Winner (\(\chi^{2}\)) [29] & 72.8\% & 72.0\% & 79.8\% & 71.9\% & 74.1\% \\ \hline Winner (EMD) [33] & 68.1\% & 74.1\% & 79.7\% & 75.3\% & 74.3\% \\ \hline PDK [34] & 70.1\% & 78.4\% & 76.9\% & 72.5\% & 74.5\% \\ \hline Xie [35] & 75.4\% & 78.2\% & 79.1\% & 73.9\% & 76.7\% \\ \hline ICBS [31] & 77.7\% & 80.0\% & 77.4\% & 77.2\% & 78.1\% \\ \hline Our Average Result & 75.5\% & 81.7\% & 78.1\% & 89.5\% & 81.2\% \\ \hline Our Best Result & 76.5\% & 83.3\% & 78.8\% & 90.3\% & 82.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results (EER) with other algorithms in VOC2005.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Times** & **Bikes** & **Vehicles** & **Motorcycles** & **Pedestrians** & **Average** \\ \hline
1 & 76.6\% & 80.6\% & 77.8\% & 88.6\% & 80.9\% \\ \hline
2 & 75.3\% & 82.4\% & 79.3\% & 89.5\% & 81.6\% \\ \hline
3 & 76.5\% & 83.3\% & 78.8\% & 90.3\% & 82.2\% \\ \hline
4 & 75.8\% & 83.3\% & 76.7\% & 89.9\% & 81.4\% \\ \hlineA comparison with GVC [37], which uses both the Gabor feature [38] and Moment feature [39] to perform SVM [10] training is shown in Table 6. The Gabor feature is usually used for age recognition and texture analysis, and the Moment feature is usually used for aircraft recognition and shape analysis. Combining these two features can extract features in terms of both detail and contour, making the classification effect better. Further Backpropagation Neural Network combines the proposed method with a single-class model, which achieved an error rate of 5.7% for aircrafts, 55.5% for motorcycles, 55.8% for vehicles, and 47.8% for faces, and thus the recognition ability for aircrafts increased. The termination conditions were iterated more than 6000 times, or the mean square error of the output from the neural network and the expected output were less than one. We performed the experiments eight times, and used the best, worst, and average values to perform further analysis, as in Table 7. We achieved an error rate of only 2.3%, which proves that even if we face a classification goal that is difficult, we can still use an external model to enhance the proposed model and achieve the ideal result.