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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 111–120 of 135 papers

TitleStatusHype
Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification—0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization—0
Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms—0
Cross-Graph Learning of Multi-Relational Associations—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Minimax Lower Bounds for Realizable Transductive Classification—0
Unsupervised Tube Extraction Using Transductive Learning and Dense TrajectoriesCode0
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning—0
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