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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 61–70 of 135 papers

TitleStatusHype
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis—0
Transductive Learning with Multi-class Volume Approximation—0
Transductive Learning with String Kernels for Cross-Domain Text Classification—0
Transductive Multi-class and Multi-label Zero-shot Learning—0
Transductive Multi-label Zero-shot Learning—0
Transductive Non-linear Learning for Chinese Hypernym Prediction—0
Transductive Rademacher Complexity and its Applications—0
Transductive Semi-Supervised Deep Learning using Min-Max Features—0
Transductive Unbiased Embedding for Zero-Shot Learning—0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
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