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Transductive Learning

In this setting, both a labeled training sample and an (unlabeled) test sample are provided at training time. The goal is to predict only the labels of the given test instances as accurately as possible.

Papers

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Showing 41–50 of 135 papers

TitleStatusHype
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora—0
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification—0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Inductive Graph Neural Networks for Moving Object Segmentation—0
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
Deep Transductive Semi-supervised Maximum Margin Clustering—0
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples—0
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