Entropic regularization is able to precisely remove meaningless peaks in DRT spectra.Entropic regularization can be recast in a Bayesian framework.L-curve method can be used to select the regularization parameter for entropic DRT.Entropic regularization is more accurate to recover multidimensional DRTs....
Entropy-based regularization of AdaBoost 来自 EBSCO 喜欢 0 阅读量: 45 作者: Micha Bereta 摘要: In this study, we introduce an entropy-based method to regularize the AdaBoost algorithm. The AdaBoost algorithm is a well-known algorithm used to create aggregated classifiers. In many real-world ...
Multi-view subspace clustering with adaptive locally consistent graph regularization Article 05 June 2021 Explore related subjects Discover the latest articles, news and stories from top researchers in related subjects. Artificial Intelligence Data Availibility The datasets selected in this paper are ...
Table 1 reports a comparison between the proposed method and two other clustering algorithms based on the incomplete Cholesky decomposition, namely algorithms (for a fair comparison, we report the results of the algorithm that does not use the L1 regularization) [18,22]. The comparison concerns ...
‘When these exits noises in the process of CS measurement, the propose algorithm can estimate the amplitude of noises, then automatically adjusts the threshold of regularization to reduce the noise effectively.’ The sentence needs to be rephrased. Author Response Please see the attachment. Author...
A vector entropy consisting of the second order entropy (Ent-2) and the cross entropy is constructed as the regularization term which incorporates the prior motion knowledge into the estimation process. By imposing the motion constraints, the vector-entropy regularization converts the ill-posed ...
To avoid overfitting, we establish a regularization term that is formulated as \({\mathscr {L}}_{reg} = \frac{1}{2}\sum _{i=0}^{I}(||W^i||_2^2 + ||\widehat{W^i}||^2_2)\), where \(W^i\) and \(\widehat{W^i}\) indicates the weight of the encoder and decoder ...
The integration of entropy pooling and regularization is critical, with their omission leading to notable reductions in R2 values. Our method also surpasses the performance of CNN and other machine-learning models, including long short-term memory (LSTM) networks and support vector machine (...
Chen, S.C., Wang, Z., Tian, Y.J.: Matrix-pattern-oriented Ho-Kashyap classifier with regularization learning. Pattern Recogn. 40(5), 533–1543 (2007) 210 C. Zhu 3. Wang, Z., Chen, S.C.: New least squares support vector machines based on matrix patterns. Neural Process. Lett. ...
-ror--reg: Specify the type of regularization. Default is L2 regularization. L1 and L2 are supported. -ior--iteration: The method relies on iterative scaling to find the optimal parameters. This option specifies the number of iterations. Generally, the more iterations the model runs, the bette...