What is semi-supervised classification?
Semi-supervised classification. Xiaojin Zhu. Univ. Wisconsin-Madison – p. 67/76 Relation to spectral clustering f can be decomposed as f = P iα iφ i f⊤∆f = X i α2 iλ i f wants basis φ iwith small λ φ’s with small λ’s correspond to clusters f is a balance between spectral clustering and obeying labeled data Semi-supervised classification.
Is there a semi-supervised classification model for graphs?
Semi-Supervised Classification with Graph Convolutional Networks Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. TL;DR: Semi-supervised classification with a CNN model for graphs. Conflicts: uva.nl
Can perturbation-based semi-supervised learning be applied to medical image classification?
Hence, the purpose of this study is to explore a new approach to perturbation-based semi-supervised learning which tackles the problem of applying semi-supervised learning to medical image classification with imbalanced training data.
What is the difference between supervised and semi-supervised learning?
The sign of an effective semi-supervised learning algorithm is that it can achieve better performance than a supervised learning algorithm fit only on the labeled training examples. Semi-supervised learning algorithms generally are able to clear this low bar expectation.
