Publisher
Springer Nature Switzerland
Reference30 articles.
1. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)
2. Vellido, A.: The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Netw. Appli. 32(24), 18069–18083 (2020)
3. Sato, A., Yamada, K.: Generalized learning vector quantization. In: Advances in Neural Information Processing Systems 8. Proceedings of the 1995 Conference, pp. 423–429 (1996)
4. Seo, S., Obermayer, K.: Soft learning vector quantization. Neural Comput. 15(7), 1589–1604 (2003)
5. Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/978-3-642-56927-2