## 4 Experimental Results
### WP-SVM Performance in Processing Chunk versus Sequential Data  
To characterize the detection performance of _WP-SYM_, we divided DB1 into 3 independent sets of data: a training set (hereafter, TrainSet), a testing set (hereafter, TestSet), and an incremental set (hereafter, IncSet) in a manner to preserve all data that belong to a patient in one of these sets. This validation technique insures exclusion of any criterion that has been optimized during the model training phase from optimistically biasing the model generalization performance in the validation step. We compared CTC classification performance when the ML model was retrained (hereafter, _Retrain_Model_) to the case where incremental learning using _WP-SVM_ was applied. In the latter case, the dynamic data were processed either in a chunk manner (hereafter, _Inc_Model_) and by incorporating the data sequentially into the classifier (hereafter, _Inc_Seq_Model_). We also compared _WP-SYM_ to the confusion rate for simple incremental SVM as compared to the _Retrain_Model_. Table 3 summarizes the average result for 20 different experiments as well as CPU requirements as normalized to the baseline of _Retrain_Model_ while using Matlab's _etime_ routine in order to insure the analysis is independent of machine specifics.  
The ratio of the confusion rates of _Inc_Model_ to the _Retrain_Model_ was found to be 1.2 on average. And for the sequential processing, the ratio of the confusion rates of the _WP-SVM_ to the _Retrain_Model_ was on average improved to a factor of 1.07 - which represents almost a 16% improvement over the chunk data processing scenario. Table 3 also shows the CPU usage times for the models normalized with respect to _Initial_Model_ CPU requirements. On average, the ratio of the CPU times of _Inc_Model_ to the _Retrain_Model_ was 0.401. We also observed a marginal degradation in the CPU time for the _Inc_Seq_Model_. The ratio of the CPU times of _Inc_Seq_Model_ to the _Retrain_Model_ was 0.675. This means that the improvement in the sequential classifier's accuracy degraded CPU usage time with respect to batch processing. However, this is a reasonable price to incur for enhancing polyp detection by almost 16% with respect to the batch processing.  
To illustrate the advantage of online learning in that incremental training could have the advantage of possibly improving classifier accuracy by being able to incorporate more data into the model over the baseline retrain method., we started with an intentionally poor-performing _Initial_Model_ to which _WP-SYM_ was applied iteratively for testing the model convergence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & _Inc_Model_ & _INC_Seq_Model_ & Incremental SVM \\ \hline Confusion Rate & 1.2 & 1.07 & 1.24 \\ CPU Time & 0.62 & 0.675 & 0.687 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized Confusion Rates and CPU Requirements with respect to _Retrain_Model_ for _Inc_Model_, _Inc_Seq_Model_, and Incremental SVM.  
As shown in Figure 4, _WP-SVM_ convergence rate for TP2 in reaching an acceptable sensitivity level is basically influenced by the size of _IncSet_. With larger _IncSet_ sizes successively applied to _Initial_Model, WP-SVM_ adjusted faster the hyper-plane positions to gradually learn classification of the CTC data without having to consume resources for retraining thus validating the viability of incremental learning in improving model parameters after some iterative training.