006.31 Maschinelles Lernen
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Diese Arbeit beschäftigt sich mit dem Erstellen semantischer Encodings von Bilddaten. Um diese Kodierungen aus den Daten zu extrahieren, wird ein künstliches neuronales Netzwerk auf
Videobild Interpolation trainiert. Die daraus erlernten Encodings sollen anschließend auf ihre Anwendbarkeit in einer anderen Aufgabe der KI gestützten Bildverarbeitung, der Extraktion von Landmarken auf Menschen, getestet werden.
Machine learning models for timeseries have always been a special topic of interest due to their unique data structure. Recently, the introduction of attention improved the capabilities of recurrent neural networks and transformers with respect to their learning tasks such as machine translation. However, these models are usually subsymbolic architectures, making their inner working hard to interpret without comprehensive tools. In contrast, interpretable models such learning vector quantization are more transparent in the ability to interpret their decision process. This thesis tries to merge attention as a machine learning function with learning vector quantization to better handle timeseries data. A design on such a model is proposed and tested with a dataset used in connection with the attention based transformers. Although the proposed model did not yield the expected results, this work outlines improvements for further research on this approach.
In this paper, we conduct experiments to optimize the learning rates for the Generalized Learning Vector Quantization (GLVQ) model. Our approach leverages insights from cog- nitive science rooted in the profound intricacies of human thinking. Recognizing that human-like thinking has propelled humankind to its current state, we explore the applica- bility of cognitive science principles in enhancing machine learning. Prior research has demonstrated promising results when applying learning rate methods inspired by cognitive science to Learning Vector Quantization (LVQ) models. In this study, we extend this approach to GLVQ models. Specifically, we examine five distinct cognitive science-inspired GLVQ variants: Conditional Probability (CP), Dual Factor Heuristic (DFH), Middle Symmetry (MS), Loose Symmetry (LS), and Loose Symme- try with Rarity (LSR). Our experiments involve a comprehensive analysis of the performance of these cogni- tive science-derived learning rate techniques across various datasets, aiming to identify optimal settings and variants of cognitive science GLVQ model training. Through this research, we seek to unlock new avenues for enhancing the learning process in machine learning models by drawing inspiration from the rich complexities of human cognition. Keywords: machine learning, GLVQ, cognitive science, cognitive bias, learning rate op- timization, optimizers, human-like learning, Conditional Probability (CP), Dual Factor Heuristic (DFH), Middle Symmetry (MS), Loose Symmetry (LS), Loose Symmetry with Rarity (LSR).
Analysis of Continuous Learning Strategies at the Example of Replay-Based Text Classification
(2023)
Continuous learning is a research field that has significantly boosted in recent years due to highly complex machine and deep learning models. Whereas static models need to be retrained entirely from scratch when new data get available, continuous models progressively adapt to new data saving computational resources. In this context, this work analyzes parameters impacting replay-based continuous learning approaches at the example of a data-incremental text classification task using an MLP and LSTM. Generally, it was found that replay improves the results compared to naive approaches but achieves not the performance of a static model. Mainly, the performances increased with more replayed examples, and the number of training iterations has a significant influence as it can partly control the stability-plasticity-trade-off. In contrast, the impact of balancing the buffer and the strategy to select examples to store in the replay buffer were found to have a minor impact on the results in the present case.
Learning Vector Quantization ist ein Klassifikator, der in seiner Urform im euklidischen Raum lernt. Für Zeitreihendaten benötigt es ein gesondertes Distanzmaß, nicht nur wegen der Relation der Zeitpunkte untereinander, sondern auch wegen der unterschiedlichen Längen dieser Zeitreihendaten. Als solches Distanzmaß wird Dynamic Time Warping eingesetzt. Diese Arbeit untersucht die Implementierung und dessen Zeit- und Raumkomplexität.
In dieser Arbeit werden die algorithmischen Grundlagen der Machine Learning Verfahren LVQ1 und LVQ3 erläutert. Für LVQ3 werden mehrere Ansätze zur Anpassung der Lernrate betrachtet, die anschließend verglichen werden sollen. Dazu werden vier verschiedene Experimente durchgeführt, wobei zwei Datensätze Verwendung finden, deren Ursprung in medizinischen Bilddaten liegt.
This thesis investigates the efficacy of four machine learning algorithms, namely linear regression, decision tree, random forest and neural network in the task of lead scoring. Specifically, the study evaluates the performance of these algorithms using datasets without sampling and with random under-sampling and over-sampling using SMOTE. The performance of each algorithm is measure using various performance metrics, including accuracy, AUC-ROC, specificity, sensitivity, precision, recall, F1 score, and G-mean. The results indicate that models trained on the dataset without sampling achieved higher accuracy than those trained on the dataset with either random under-sampling or random over-sampling using SMOTE. However, the neural network demonstrated remarkable results on each dataset compared to the other algorithms. These findings provide valuable insights into the effectiveness of machine learning algorithms for lead scoring tasks, particularly when using different sampling techniques. The findings of this study can aid lead management practices in selecting the most suitable algorithm and sampling technique for their needs. Furthermore, the study contributes to the literature by providing a comprehensive evaluation of the performance of machine learning algorithms for lead scoring tasks. This thesis has practical implications for businesses looking to improve their lead management practices, and future research could extend the analysis to other machine learning algorithms or more extensive datasets.
Das Ziel dieser Masterarbeit ist die Evaluierung des Realtime Multi-Person 2D Pose Estimation Frameworks OpenPose. Dazu wird die Forschungsfrage gestellt, bis zu welcher Pixelgröße ein Mensch allgemein von dem System mit einer Sicherheit von über 50% richtig detektiert und dargestellt wird. Um die Forschungsfrage zu beantworten ist eine Studie mit sieben Probanden durchgeführt wurden. Aus der Datenerhebung geht hervor, dass der gesuchte Confidence Value zwischen 110px und 150px Körpergröße in von Menschen digitalen Bildern erreicht wird.
In machine learning, Learning Vector Quantization (LVQ) is well known as supervised vector quantization. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [2]. In many tasks of classification, different variants are considered while training a model and a consideration of variants of large margin in LVQ helps to get significant
results [20]. Large margin LVQ (LMLVQ) is to maximize the distance between decision hyperplane and data points. In this thesis, a comparison of different variants of Generalized Learning Vector Quantization (GLVQ) and Large margin in LVQ is proposed along with visualization, implementation and experimental results.
Die vorliegende Arbeit beschäftigt sich mit der KI-gestützten Klassifikation von Flügelbildern verschiedener Spezies der Familie Calliphoridae, auch Schmeißfliege genannt. Hauptziel soll dabei die Klassifikation nach Gattung sowie nach Spezies sein. Außerdem soll eine automatische Landmarkendetektion auf Fliegenflügeln entwickelt werden und anschließend als Merkmalsextraktor für das Klassifikationsmodell dienen. Dabei werden unterschiedliche Methoden der Bildverarbeitung sowie des maschinellen Lernens angewandt, kombiniert und bezüglich der Ergebnisse analysiert und verglichen.