DP-GMM clustering-based ensemble learning prediction methodology for dam deformation considering spatiotemporal differentiationDeformation predictionSpatiotemporal differentiationMulti-output ensemble learningSpatiotemporal clusteringSynchronous optimizationThe deformation behavior can effectively reflect the health status of...
Unsupervised Linear Discriminant Analysis for Supporting DPGMM Clustering in the Zero Resource Scenario The idea is to automatically find a mapping of feature vectors into a subspace that is more suitable for Dirichlet process Gaussian mixture model (DPGMM)... M Heck,S Sakti,S Nakamura - 《Proced...
Then, within the framework of this kink estimation algorithm, the max-min distance method is used to select the initial clustering centers for a K-means type algorithm to optimize the model parameters for each group. The number of groups is determined using an automated elbow method. Numerical ...
136(机器学习理论篇3)8.4 Hierarchical clustering 层次聚类应用 - 2 14:25 137(机器学习理论篇3)8.4 Hierarchical clustering 层次聚类应用 - 3 14:02 138(机器学习理论篇3)9 神经网络NN算法 - 1 18:48 139(机器学习理论篇3)9 神经网络NN算法 - 2 19:07 140(机器学习理论篇3)9 神经网络NN算法 - ...
Nakamura, "Unsupervised linear dis- criminant analysis for supporting DPGMM clustering in the zero resource scenario," in Proc. SLTU, 2016, pp. 73-79.M. Heck, S. Sakti, and S. Nakamura, "Unsupervised linear dis- criminant analysis for supporting DPGMM clustering in the zero resource ...