Specifically, two classification modules based on the kernel ridge regression (KRR) are learned for the two types of features, and they are integrated via a joint model. With the joint model, the classification based on visual features can be reinforced by the classification based on textual ...
Several kernel ridge regression classifiers are constructed from different training subsets in each base classifier. The partitioning of the training samples into different subsets leads to a reduction in computational complexity when calculating matrix inverse compared with the standard approach of using ...
对此, 提出一种基于Multi-kernel 和KRR的数据还原算法. 首先, 通过同类数据中已知数据进行多次核化迭代, 使已知数据在超高维欧氏空间中呈线性; 然后, 利用已知数据对同类未知数据进行线性表示, 并以Kernel ridge regression (KRR) 算法进行未知数据的回归; 最后实现数据还原. 选取Iris flower 和JAFFE两类数据集...
Li X, Zhong X, Shao H, Han T, Shen C (2021) Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression. Reliab Eng Syst Saf 216:108018. https://doi.org/10.1016/J.RESS.2021.108018 Article Google Scholar Tao L et al (2022...
(EANN)44. These models employ either hand-crafted or machine-learned descriptors of atomic environments, along with deep neural networks, to approximate potential energy. Other machine learning techniques, such as kernel ridge regression, are also widely used. Examples include the Gaussian approximation...
Kernel based methodsNeural controlRegularizationRidge regressionRobust estimationStatistical learning theorySupport vector machinesIn recent years neural networks as ... JAK Suykens - 《European Journal of Control》 被引量: 341发表: 2001年 Data classification with radial basis function networks based on a...
The first strategy is to treat each state separately in a kernel ridge regression model and all states together in a multiclass neural network. The second strategy is to instead encode the state as input into the model, which is tested with both models. Numerical evidence suggests that using ...
In this paper we study multi-task kernel ridge regression and try to understand when the multi-task procedure performs better than the single-task one, in terms of averaged quadratic risk. In order to do so, we compare the risks of the estimators with pe
ML: Machine learning QC: Quality control RF: Random Forest RKHS: Reproducing kernel Hilbert spaces regression SVR: Support vector regression ssGBLUP: Single-step BLUP SF: Shear force wmssBLUP: Weighted multi-omics single-step BLUP WHC: Water holding capacity ...
3.1. Simplified Multi-kernel Correlation Filter The goal of a ridge regression [45] is to solve the Tikhonov regularization problem, min f 1 2 l−1 ∑(f (xi) − yi)2 + λ||f ||2k , i=0 (1) 4875 where l is the number of samples, f lies in a bounded con- vex subset ...