## 5 Application of the MAME-ZsL in Hyperspectral Image Analysis
This is done in order to handle more general problematic structures, even discerning the minimal similarities or anomalies they entail. Specifically, since the aim was the design of a system with zero samples from the target class, the proposed methodology used the intermediate representations extracted from the rest of the image samples. This was done in order to find the appropriate representations to be used in order to classify the unknown image samples. To increase the efficiency of the method, bootstrap sampling was used, in order to train different subsets of the data set in the most appropriate way. Bootstrap sampling is the process of using increasingly larger random samples until the accuracy of the neural network is improved. Each sample is used to compile a separate model and the results of each model are aggregated with \"voting\"; that is, for each input vector, each classifier predicts the output variable, and finally, the value with the most \"votes\" is selected as the response variable for which particular vector. This methodology, which belongs to the ensemble methods, is called bagging and has many advantages, such as reducing co-variance and avoiding overfitting, as you can see in the below Figure 5[31]. The ensemble approach was selected to be employed in this research, due to the particularly high complexity of the examined ZsL, and due to the fact that the prediction results were highly volatile. This can be attributed to the sensitivity of the correlational models to the data, and to the complex relationship which describes them. The ensemble function of the proposed system offers a more stable model with better prediction results. This is due to the fact that the overall behavior of a multiple  
Figure 5: Bagging (bootstrap sampling) ([[https://www.kdnuggets.com/](https://www.kdnuggets.com/)]([https://www.kdnuggets.com/](https://www.kdnuggets.com/))). model is less noisy than a corresponding single one. This reduces the overall risk of a particularly bad choice. It is important to note that in Deep Learning, the training process is based on analyzing large amounts of data. The research and development of neural networks is flourishing thanks to recent advancements in computational power, the discovery of new algorithms and the increase in labeled data. Neural networks typically take longer to run, as an increase in the number of features or columns in the dataset also increases the number of hidden layers. Specifically, we should say that a single affine layer of a neural network without any non-linearities/activations is practically the same as a linear model. Here we are referring to deep neural networks that have multiple layers and activation functions (non-linearities as Relu, Elu, tanh, Sigmoid) Additionally, all of the nonlinearities and multiple layers introduce a nonconvex and usually rather complex error space, which means that we have many local minimums that the training of the deep neural network can converge to. This means that a lot of hyperparameters have to be tuned in order to get to a place in the error space where the error is small enough so that the model will be useful. A lot of hyper parameters which could start from 10 and reach up to 40 or 50 are dealt with via Bayesian optimization, using Gaussian processes to optimize them, which still does not guarantee good performance. Their training is very slow, and adding the tuning of the hyperparameters into that makes it even slower, whereas the linear model would be much faster to be trained. This introduces a serious cost-benefit tradeoff. A trained linear model has weights which are interpretable and gives useful information to the data scientist as to how various features should have roles for the task at hand. Modern frameworks like TensorFlow or Theano perform execution of neural networks on GPU. They take advantage of parallel programming capabilities for large array multiplications, which are typical of backpropagation algorithms. The proposed Deep Learning model is a quite resource-demanding technology.