Ensemble and multi-task learning frameworks have also been developed for drug response prediction8,9,24,29. In this paper, rather than developing a new algorithm for drug response prediction, we propose a transfer learning framework that can improve the prediction performance of existing algorithms ...
A novel ensemble-based transfer learning algorithm called Trbaggboost is proposed, which uses small amount of labeled data from a new subject along with labelled data from other subjects to train an ensemble of learners for predicting unlabeled data from the new subject. Conventional machine ...
Ensemble Transfer Learning Algorithm Liu, Xiaobo,Liu, Zhentao,Wang, Guangjun,... - 《IEEE Access》 - 2018 - 被引量: 1 PETLFC: Parallel ensemble transfer learning based framework for COVID-19 differentiation and prediction using ...
Algorithm 1 Algorithm of proposed model Full size table The composite model was compiled using the Adam optimizer, which adjusts the learning rate throughout training, and sparse categorical cross entropy as the loss function, ideal for multi-class classification of mutually exclusive classes. We cho...
An integrated federated learning algorithm for short-term load forecastingClusteringDecomposition-ensemble methodFederated learningLoad forecasting... Y Yang,Z Wang,SWJ Zhao - 《Electric Power Systems Research》 被引量: 0发表: 2023年 Online transfer learning-based residential demand response potential forec...
Classification and regression tree (CART)Cluster analysisComplex dataEnsemble algorithmKernel methodOutlier detectionPattern analysisTree-based ensemble... XLX Zhang - 《Trac Trends in Analytical Chemistry》 被引量: 7发表: 2012年 Using Multi-features and Ensemble Learning Method for Imbalanced Malware Cla...
The Levenberg-Marquardt Algorithm for Nonlinear Least Squares Curve-Fitting Problems; Duke University: Durham, NC, USA, 2019; pp. 1–19. [Google Scholar] Voglis, C.; Lagaris, I. A rectangular trust region dogleg approach for unconstrained and bound constrained nonlinear optimization. In ...
Deep learning neural networks are nonlinear methods. They offer increased flexibility and can scale in proportion to the amount of training data available. A downside of this flexibility is that they learn via a stochastic training algorithm which means that they are sensitive to the specifics of ...
Informative article and a very good/simple introduction to ensemble methods yoon thanks. good posting Sayak Paul Any hands on example would have been of great help. But I really liked how you incorporated the error terms and how they impact the Ensemble Learning process. Overall a very good re...
[5,35] proposed to use intermediate young parent face images, and developed a transfer subspace learning (TSL) based algorithm to bridge the divergence between the children and old parent images. Show abstract Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D ...