Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and ; Proceedings of the 1 Thomas Villmann,

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https://doi.org/10.1007/978-3-322-83403-4pecific use case, it is frequently adequate to compute only a subset of dominant eigenvectors or utilize estimations. Handling this task for large matrices poses a challenge, as standard machine learning packages often lack suitable implementations. We explores various techniques for approximating d

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Gruppenarbeit in der industriellen Praxis,irichlet Allocation) have significant drawbacks, including complex parameter settings. Additionally, these methods often yield low-quality results. Therefore, improving the outcomes of topic modeling is a crucial goal. In this paper, we compare the performance of LDA with a recent topic modeling app

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https://doi.org/10.1007/978-3-322-83403-4ntify their relevance, either with respect to a local decision or a global model. Feature relevance determination constitutes a foundation for feature selection, and it enables an intuitive insight into the rational of model decisions. Indeed, it constitutes one of the oldest and most prominent expl

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https://doi.org/10.1007/978-3-322-83403-4 for prototype-based models with the emphasis on interpretability. In this regard, we will show how the learning rules are associated to the underlying decision making of such models. Moreover, the work concludes by giving possible interpretations of these rules and anchor points for developing rela

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Conference proceedings 2024 (WSOM$+$ 2024), held at the University of Applied Sciences Mittweida (UAS Mitt-weida), Germany, on July 10–12, 2024..The book highlights new developments in the field of interpretable and explainable machine learning for classification tasks, data compression and visualization. Thereby, the main fo

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Dieter Sandner Dipl.-Psych. M. A.ervised learning of the Growing Self-Organizing Array (GSOA) modified to address the constrained minimal data retrieving time. The proposed method is compared with a baseline based on a sampling-based decoupled approach, and the results support the feasibility of both proposed solvers in random instances.

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https://doi.org/10.1007/978-3-642-71247-0erpretable models. Finally, we show the ability to maintain group properties in the projection space. Due to these applications, deep projection pursuit is a flexible design paradigm with various use cases.

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Gruppenarbeit in der industriellen Praxis,s. We also highlight the benefits of post-processing the clustering results before modeling topics in the CFMf approach. Our reference dataset consists of 16,917 full-text articles on the philosophy of science.