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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 51–75 of 135 papers

TitleStatusHype
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
DC Proximal Newton for Non-Convex Optimization Problems—0
Deep Domain Adaptation under Deep Label Scarcity—0
Deep Transductive Semi-supervised Maximum Margin Clustering—0
Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents—0
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification—0
Transductive Learning for Multi-Task Copula Processes—0
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models—0
Transductive Learning for Zero-Shot Object Detection—0
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
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Understanding Generalization via Leave-One-Out Conditional Mutual Information—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
VLSI Hypergraph Partitioning with Deep Learning—0
Bayesian Circular Regression with von Mises Quasi-Processes—0
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