\mathcal{L}(f)=\underbrace{\mathcal{L}_s\left(f, \mathcal{D}_l\right)}_{\text {supervised loss }}+\lambda \underbrace{\mathcal{L}_u\left(f, \mathcal{D}_u\right)}_{\text {unsupervised loss }}+\mu \underbrace{\mathcal{L}_r(f, \mathcal{D})}_{\text {regularization los...
图构造技术总结--Graph‑based semi‑supervised learning via improving the quality of the graph dynamically 前言 本博文主要对论文中提到的图构造方法进行梳理,论文自己提出的模型并未介绍,感兴趣的可以阅读原文 摘要 基于图的半监督学习GSSL主要包含两个过程:图的构建和标签推测。传统的GSSL中这两个过程是完全...
Mark Culp,George Michailidis.Graph-Based Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence . 2008Graph-based semi-supervised learning[J] . Changshui Zhang,Fei Wang.Artificial Life and Robotics . 2009 (4)Changshui Zhang,Fei Wang.Graph-based semi-supervised ...
Recent years have witnessed a surge of interest in graph-based semi-supervised learning. However, two of the major problems in graph-based semi-supervised learning are: (1) how to set the hyperparameter in the Gaussian similarity; and (2) how to make the algorithm scalable. In this article...
Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can improve the predictive performance. Empirical examples are included to illustrate the main consequences of our analysis. 展开 关键词: Graph-based semi-supervised learning kernel design transductive...
RL 强化学习 Reinforcement Learning 72024-09 2 图半监督学习 Graph-based Semi-Supervised L… 42024-09 3 S3VM 半监督支持向量机 52024-09 4 SSL 半监督学习 Semi-Supervised Learning 32024-09 5 Eclat 等价类聚类和自底向上的格遍历 12024-09 6 Apriori算法(尿布啤酒) 62024-09 7 关联规则学习 Associatio...
【论文笔记】:2020-CVPR-Shoestring Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data 1.主要问题 基于图的半监督分类,图嵌入+度量学习 2.研究现状 在半监督学习(SSL)中,少量标记的样本与相对大量的未标记样本一起用于分类。在现有的半监督学习模型中,基于图的方法(例如图卷积网络和...
Kipf T, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations Li Y, Zhang S, Cheng D, He W, Wen G, Xie Q (2017) Spectral clustering based on hypergraph and self-re-presentation. Multimed Tools Appl 76(16):...
Semi-Supervised Contrastive Learning 形式上,无监督对比学习有望达到如下效果: score(f(xi),f(x+i))≫score(f(xi),f(x−i))score(f(xi),f(xi+))≫score(f(xi),f(xi−)) 也就是正样本之间的距离远远小于负样本对之间的距离,其中ff是编码器,提出的无监督对比损失如下: ...
Graph-based Semi-Supervised Learning with Multiple Labels 热度: Graph-based semi-supervised learning with multi-modality… 热度: Semi-supervisedLearningwithGraphLearning-ConvolutionalNetworks BoJiang,ZiyanZhang,DoudouLin,JinTang ∗ andBinLuo SchoolofComputerScienceandTechnology,AnhuiUniversity,Hefei,230601,Chi...