SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 135 papers

TitleStatusHype
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries—0
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling—0
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning—0
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning—0
Regularization and Optimal Multiclass Learning—0
Relax and Randomize : From Value to Algorithms—0
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives—0
Robust Collective Classification against Structural Attacks—0
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines—0
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization—0
Show:102550
← PrevPage 10 of 14Next →

No leaderboard results yet.