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Adversarial robustness of a nearest prototype classifier assures safe deployment in sensitive use fields. Much research has been conducted on artificial neural networks regarding their robustness against adversarial attacks, whereas nearest prototype classifiers have not chalked similar successes. This thesis presents the learning dynamics and numerical stability regarding the Crammer-normalization and the Hein-normalization for adversarial robustness of nearest prototype classifiers. Results of conducted experiments are penned down and analyzed to ascertain the bounds given by Saralajew et al. and Hein et al. for adversarial robustness of nearest prototype classifiers.
Recently a deep neural network architecture designed to work on graph- structured data have been capturing notice as well as getting implemented in various domains and application. However, learning representation (feature embedding) from graphical data picking pace in research and constructing graph(s) from dataset remains a challenge. The ability to map the data to lower dimensions further makes the task easier while providing comfort in applying many operations. Graph neural network (GNN) is one of the novel neural network models that is catching attention as it is outperforming in various applications like recommender systems, social networks, chemical synthesis, and many more. This thesis discusses a unique approach for a fundamental task on graphs; node classification. The feature embedding for a node is aggregated by applying a Recurrent neural network (RNN), then a GNN model is trained to classify a node with the help of aggregated features and Q learning supports in optimizing the shape of neural networks. This thesis starts with the working principles of the Feedforward neural network, recurrent units like simple RNN, Long short-term memory (LSTM), and Gated recurrent unit (GRU), followed by concepts of Reinforcement learning (RL) and the Q learning algorithm. An overview of the fundamentals of graphs, followed by the GNN architecture and workflow, is discussed subsequently. Some basic GNN models are discussed in brief later before it approaches the technical implementation details, the output of the model, and a comparison with a few other models such as GraphSage and Graph attention network (GAN).
Anomaly Detection is a very acute technical problem among various business enterprises. In this thesis a combination of the Growing Neural Gas and the Generalized Matrix Learning Vector Quantization is presented as a solution based on collected theoretical and practical knowledge. The whole network is described and implemented along with references and experimental results. The proposed model is carefully documented and all the further open researching questions are stated for future investigations.
Convolutional Neural network (CNN) has been one of most powerful and popular preprocessing techniques employed for image classification problems. Here, we use other signal processing techniques like Fourier transform and wavelet transform to preprocess the images in conjunction with different classifiers like MLP, LVQ, GLVQ and GMLVQ and compare its performance with CNN.
This Bachelor thesis investigates the learning rules of the Hebbian, Oja and BCM neuron models for their convergence to, and the stability of, the fixed points. Existing research is presented in a structured manner using consistent notation. Hebbian learning is neither convergent nor stable. Oja learning converges to a stable fixed point, which is the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the input data. BCM learning converges to a fixed point which is stable, when assuming a discrete distribution of orthogonal inputs that occur with equal probability. Hebbian learning can therefore not be used in further applications, where convergence to a stable fixed point is required. Furthermore, this Bachelor thesis came to the conclusion that determining the fixed points of the BCM learning rule explicitly involves extensive calculation and other methods for verifying the stability of possible fixed points should be considered.