Considering the robustness in the semi-supervised DA, we replace the traditional l 2 -norm based least squares regression with the l 2, 1 -norm sparse regression, and then construct the SFSR-graph based semi-su
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. [pdf] [code] Neal Jean, Sang Michael Xie, Stefano Ermon. NeurIPS 2018 2017 Learning Safe Prediction for Semi-Supervised Regression. [pdf] Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou. AAAI 2017...
Clustering is a popular unsupervised learning task, whereas classification and regression are common supervised learning tasks. On the one hand, the main drawback of supervised learning is that data with the right features are needed: there must be sufficient data that must be properly classified ...
The supervised loss consists of a classification loss function Lce and a bounding box regression loss function L1, as shown in Eq. 1. $$L_S=\sum\limits_s\left[\frac1n\sum\limits_iL_{ce}\left(P\left(c_i\right|\alpha(Xs)),G(c_i)\right)+\frac\lambda n\sum\limits_iG\left(c_...
A multi-scheme semi-supervised regression approach. [pdf] Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas. Pattern Recognition Letters (2019) Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme. [pdf] Nikos Fazakis, Vasileios G. Kana...
在于:将domain adaptation,semi-supervisedlearning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...。模型则包含基于域适应的对抗学习和基于图像的半监督学习。数据来源是 two real-world crisis datasets collected from Twitter。 作者做这个模型的立意 ...
Jiang M, Liang Y, Feng X, Fan X, Pei Z, Xue Y, Guan R (2018) Text classification based on deep belief network and softmax regression. Neural Comput Appl 29:01 Google Scholar Jing L (2018) Online fake comments detecting model based on feature analysis. In: 2018 International conference...
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...
For comparison, we also included three semi-supervised (transductive) learning algorithms, which can be used as baselines to other domain adaptation methods: Laplacian SVM (LapSVM) Laplacian ridge regression (LapRR) Transducive SVM (TSVM)
Unveiling Alzheimer’s disease through brain age estimation using multi-kernel regression network and magnetic resonance imaging Computer Methods and Programs in Biomedicine, Volume 261, 2025, Article 108617 Raveendra Pilli,…, R. Murugan Towards practical and privacy-preserving CNN inference service for ...