To overcome the limitations of traditional machine learning methods, we propose novel Deterministic (DetMKTL) and Stochastic Multiple-Kernel Transfer Learning (StoMKTL) algorithms that are based on transfer learning. These algorithms leverage multiple kernel functions to capture complex, non-linear ...
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本人原创的对下面文章的解读：Domain Transfer Multiple Kernel Learning Duan, L.; Tsang, I.; Xu, ...
This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model. 展开 关键词: Multiple kernel learning ...
Transfer learning has benefitted many real-world applications where labeled data are abundant in source domains but scarce on the target domain. As there are usually multiple relevant domains where knowledge can be transferred, Multiple Source Transfer Learning (MSTL) has recently attracted much attenti...
Domain Transfer SVM for video concept detection Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods... L Duan,WH Tsang,X Dong,... - IEEE Conference on Computer Vision & Pattern Recognition ...
kernelmethods;1999.MITPress. 29 17.McCallumA,FreitagD,PereiraF.umentropyMarkovmodelsforinformation extractionandsegmentation.ProceedingsoftheSeventeenthInternationalConferenceon MachineLearning;2000. 18.LaffertyJD,McCallumA,PereiraFCN.ConditionalRandomFields:ProbabilisticModels forSegmentingandLabelingSequenceData.Proceed...
We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Result...
some excellent deep learning technology has been developed and applied to credit scoring (Gunnarsson et al., 2021). e.g., Convolutional Neural Network (CNN) (Dastile & Celik, 2021), Graph convolutional network(GCN) (Lee, Lee, & Sohn, 2021), deep multiple kernel learning (Wu et al., 20...
In the field of tribology, many studies now use machine learning (ML). However, ML models have not yet been used to evaluate the relationship between the friction coefficient and the elemental distribution of a tribofilm formed from multiple lubricant additives. This study proposed the possibility...