https://www.selleckchem.com/products/sf2312.html
The universal feature space is constructed by leveraging the relatednesses among features. We propose a generative graphical model to model the construction process, and show that learning from the universal feature space can effectively improve the performance with theoretical guarantees. The experimental results demonstrate that GLSC achieves conspicuous performance on both synthetic and real data sets.Multitask Gaussian processes (MTGPs) are a powerful approach for modeling dependencies between multiple related tasks or functions for
Like
Comment
Share
