N_labels：每个实例的平均标签数。Return_indicator：如果为True，“sparse”则以稀疏的二进制指示器格式...
Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125, 91–101 [71] Termeh, S.V.R., Kornejady, A., Pourghasemi, H.R., Keesstra, S., 2018. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference ...
You can then use these indicator variables as explanatory variables in the Multiscale Geographically Weighted Regression tool. This tool outputs a feature class and adds fields with the local diagnostic values. The Output Features values and associated charts are automatically added to t...
While SLEAP supports this functionality, we opted for an approach based on integral regression35 (see Part localization for details). We made this decision as integral regression is extremely fast at inference time and requires no additional loss term or costly optimization of an additional output ...
Product design time forecast using relative entropy kernel regression Int J Indus Eng, 26 (3) (2019) Google Scholar [138] A. Krylovas, S. Dadelo, N. Kosareva, E.K. Zavadskas Entropy–KEMIRA approach for MCDM problem solution in human resources selection task Int J Inf Technol Decis Ma...
regression using Fully Connected Layers is performed. The Mask R-CNN model additionally makes a mask prediction at this point. The loss minimised during training takes into account the accuracy of the described three model predictions, namely classification, bounding box regression, and mask extraction...
(2001). A kernel method for multi-labeled classification. In Proceedings of the neural information processing systems (pp. 681–687). Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Analytical methods for social research. New York: Cambridge ...
Briefly, the RVM belongs to a set of sparse machine-learning approaches that builds a classification/regression function from a weighted linear combination of kernel functions, in which the weights are tuned during the learning phase to produce an optimal classification of the training data (...
Choice of kernel function and parameters In order to construct the SVM model, a dataset of 70 tunnel collapses was collected from the study2and classified according to Table1. The dataset is used as training data, and the optimal hyperparameters (C,γ) of the SVM model are found using the...
and ethnicity were included as covariates. A random intercept term was used to estimate the variability between individuals in the study and thus account for repeated measures. We performed differential expression analyses for the transcriptomic data and the proteomic data. The regression model for thes...