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4.2 Gaussian processes It is a difficult task to apply deep learning models to problems with scarce training data. In this case, it would be interesting to try additionally other ML methods that make full use of all available data. Bayesian models are particularly suitable for this situation and, in particular, the models based on Gaussian Processes (GP) [133]. This is the reason to test the suitability to combine a deep learning architecture with a GP model. This has been done in [4], where a small amount of training data was available to train the QoE classifier. GP models are generally applied to regression problems, but they can also be used for classification. A GP Classifier [134] is based on the so-called Laplace approximation [134], which tries to approximate with a Gaussian function a non-Gaussian posterior formed by applying a logistic link function to the output of an intermediate latent function. The resulting squashed outcomes produced by the link function are associated to classification probabilities. The kernel [133] chosen has been a Radial Basis Function (RBF) kernel with two adjustable parameters: lengthscale and variance. To train the GP Classifier consists on tuning these two parameters plus an additional noise variance parameter associated with the likelihood of the model. When applying the GP Classifier in [4] we have used the architecture in Fig 5 as our initial network, then using one of the last layers of this network (already trained) as the input to the GP Classier (Fig. 11). With this configuration, we have trained the GP Classifier by adjusting the lengthscale and variance parameters of the RBF kernel used in this case. Considering the difficulties to apply a multi-label GP classifier [134], in [4] we have applied 14 independent GP binary classifiers: one per label (7) and prediction time-step (2). The GP classifier is a non-parametric algorithm that makes full use of the data available, which is precisely the reason to try this model in [4], since to train the QoE classifier we started with a reduced amount of training data. Nevertheless, the increase in performance obtained with the addition of the GP classifier is quite small and is not worthy of consideration, especially given the additional memory and time processing required by adding the GP classifier. In particular, in [4] we obtain and increase in the aggregated F1 measure (for all labels and time-ahead steps) from 0.6965 to 0.6987 (0.3% increase). Doctoral Thesis: Novel applications of Machine Learning to NTAP - 47PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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