Fuzzy clustering in cascaded feature space
The success of fuzzy clustering heavily relies on the proper feature space constructed by the
input data. For nonspherical and overlapped clusters, kernel fuzzy clustering is more …
input data. For nonspherical and overlapped clusters, kernel fuzzy clustering is more …
Towards kernelizing the classifier for hyperbolic data
M Yang, Q Liu, X Sun, N Shi, H Xue - Frontiers of Computer Science, 2024 - Springer
Data hierarchy, as a hidden property of data structure, exists in a wide range of machine
learning applications. A common practice to classify such hierarchical data is first to encode …
learning applications. A common practice to classify such hierarchical data is first to encode …
Clustering and Feature Spaces
YP Zhao - 2021 - search.proquest.com
Clustering and Feature Spaces Page 1 Clustering and Feature Spaces by Zhao YinPing
Doctor of Philosophy in Computer Science 2021 Faculty of Science and Technology University …
Doctor of Philosophy in Computer Science 2021 Faculty of Science and Technology University …
On Development of Data Science and Machine Learning Applications in Databricks
W Ruan, Y Chen, B Forouraghi - World Congress on Services, 2019 - Springer
Databricks is a unified analytics engine that allows rapid development of data science
applications using machine learning techniques such as classification, linear and nonlinear …
applications using machine learning techniques such as classification, linear and nonlinear …