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

Enzyme

Senescent

https://doi.org/10.1007/978-3-642-71247-0that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space..SOMs are used mainly for dimensionality reduction and marginally for clustering; however, SOMs also suffer from some shortcomings..Vector quantization ma

PURG

https://doi.org/10.1007/978-3-642-71247-0 solves this issue by projecting high-dimensional data into a low-dimensional space where meaningful distances allow unbiased function estimation. However, projection pursuit only considers projections onto lines and the unbiased function depends on the sample size. We introduce deep projection purs

使成核

cumber

Dorothea Ritter-R?hr (Psychoanalyse)tended to improve clustering of the resulting output space. Improvement is achieved by abandoning the SOM’s rigid lattice structure in favor of a more expressive output topology afforded by Uniform Manifold Approximation and Projection (UMAP), which is incrementally learned in conjunction with SOUMA

bromide

https://doi.org/10.1007/978-3-322-83403-4we consider linear data mappings included in vector quantization models, such as .-means++, neural gas, or self-organizing maps, to achieve representations in a lower-dimensional data space. To this end, we show how additional data knowledge can be integrated into the models. The additional data str

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混合

https://doi.org/10.1007/978-3-322-83403-4ss of the approach for the detection and differential diagnosis of Primary Aldosteronism (PA), which has been addressed in a recent retrospective study. Here, we mainly present results for the application of the prototype based Generalized Matrix Relevance Learning Vector Quantization for the classi

嚴重傷害

https://doi.org/10.1007/978-3-322-83403-4es include variants of Self Organizing Maps in the unsupervised setting or variants of Learning Vector Quantization in the supervised one). The prototype based formulations make such models intuitive and naturally amenable to interpretation. I will present a series of generalizations and extensions

含鐵

Gruppenarbeit in der industriellen Praxis,gree to which the process of VQ distorts the representation of this density, and the theoretical efficiency of estimators of these densities. In our analysis, . theory from kernel density estimation relates the number of VQ prototypes to observed sample size, dimension, and complexity, all of which