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Advances in self-organizing maps, learning vector quantization, interpretable machine learning, and beyondproceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in self-organizing maps, learning vector quantization, interpretable machine learning, and beyondedited by Thomas Villmann ... [et al.].
其他題名:	proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 /
其他題名:	WSOM+ 2024
其他作者:	Villmann, Thomas.
團體作者:
出版者:	Cham :Springer Nature Switzerland :2024.
面頁冊數:	xiii, 228 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science)
電子資源:	https://doi.org/10.1007/978-3-031-67159-3
ISBN:	9783031671593$q(electronic bk.)

Advances in self-organizing maps, learning vector quantization, interpretable machine learning, and beyondproceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 /
Advances in self-organizing maps, learning vector quantization, interpretable machine learning, and beyondproceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 /[electronic resource] :WSOM+ 2024edited by Thomas Villmann ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xiii, 228 p. :ill. (some col.), digital ;24 cm. - Lecture notes in networks and systems,10872367-3389 ;. - Lecture notes in networks and systems ;v.1..

Unsupervised Learning-based Data Collection Planning with Dubins Vehicle and Constrained Data Retrieving Time -- Hyperbox GLVQ Based on Min Max Neurons -- Sparse clustering with K means which penalties and for which data -- Is t SNE Becoming the New Self organizing Map Similarities and Differences -- Pursuing the Perfect Projection A Projection Pursuit Framework for Deep Learning -- Generalizing self organizing maps large scale training of GMMs and applications in data science -- A Self Organizing UMAP For Clustering -- Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation -- Exploring data distributions in Machine Learning models with SOMs -- Interpretable Machine Learning in Endocrinology a Diagnostic Tool in Primary Aldosteronism -- The Beauty of Prototype Based Learning.

ISBN: 9783031671593$q(electronic bk.)

Standard No.: 10.1007/978-3-031-67159-3doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Advances in self-organizing maps, learning vector quantization, interpretable machine learning, and beyondproceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 /
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