The domain adaptation becomes a simple regression between feature maps and does not require annotations on the new dataset. This method reaches performances similar to classic transfer learning on the PASCAL VOC dataset with synthetic transformations....
Traditional supervised learning methods such as logistic regression, random forest, and naive Bayes are suboptimal for modeling longitudinal processes as they cannot account for intertemporal associations in either outcomes or features. Recurrent neural networks (RNNs), designed for sequence data and well...
refers to the statistical distribution of the source of a dataset [10]. Domain shift refers to a change in the statistical distribution of samples, which can be due to covariate shift, the presence of open sets, or both. In gene expression datasets, the covariate shift between real data and...
在于:将domain adaptation,semi-supervised learning两部分融合到一个统一的深度学习网络中。该网络中包含基本的CNN网络和domain adaptation部分以及semi-supervised learning部分。semi-supervised learning来学习内部的代表特征通过预测图中的上下文节点(该图则将labeled和unlabeled训练数据智能...
Classification and regression by randomForest. R News. 2002;2(3):18–22. Google Scholar Balakrishnan L, Milavetz B. Decoding the histone H4 lysine 20 methylation mark. Crit Rev Biochem Mol Biol. 2010;45(5):440–52. Article CAS PubMed Google Scholar Beck DB, Oda H, Shen SS, ...
Learning Safe Prediction for Semi-Supervised Regression. [pdf] Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou. AAAI 2017 2015Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data. [pdf] Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Ga ̈el Varoqu...
Domain adaptation Conformal prediction Generative adversarial Networks 1. Introduction Scientists in the domain of cognitive psychology have long studied the relationship between facial and vocal cues in humans [1], [2]. In particular, researchers suggested that infants, during the development of their ...
However, typical state-of-the-art deep models usually require enormous amount of labeled data for training in supervised learning missions like classification and regression. For some specific fields where data labeling is utterly time-consuming and expensive, the models must exploit unlabeled data ...
The graph neural network architecture of node classification could even be extended to graph classification for looking at multiple patient samples, as is common in mass cytometry, or regression to predict continuous variables such as cellular pseudotime in the context of differentiation, cell cycle age...
However, most of the existing methods have focused only on domain adaptation and failed to achieve effective collaboration with label estimation. In this paper, an EEG feature transfer method combined with semi-supervised regression and bipartite graph label propagation (TSRBG) is p...