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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 31–40 of 135 papers

TitleStatusHype
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer—0
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications—0
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models—0
Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach—0
Regularization and Optimal Multiclass Learning—0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns—0
Fast Online Node Labeling for Very Large GraphsCode0
Estimating class separability of text embeddings with persistent homology—0
Inductive Graph Neural Networks for Moving Object Segmentation—0
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