Learning from Partial Labels 来自 Semantic Scholar 喜欢 0 阅读量: 353 作者:T Cour,B Sapp,B Taskar 摘要: We address the problem of partially-labeled multiclass classification, where instead of a single label per instance, the algorithm is given a candidate set of labels, only one of which ...
Journal of Machine Learning Research 12 (2011) 1225-1261 Submitted 10/10; Revised 2/11; Published 4/11 Learning from Partial Labels Timothee Cour @ NEC Laboratories America 10080 N Wolfe Rd # Sw3350 Cupertino, CA 95014, USA Benjamin Sapp ***@ Ben Task
为了解决这样的问题，我们提出了偏多标记学习框架(Partial Multi-label Learning, PML)。首先来看一个现实...
Nonetheless, the disambiguation strategy isprone to bemisled by the false positive labelsco-occurring withground-truth label 但是,基于消岐的策略很容易被与真相标签同时出现的伪标签误导 In this paper, a new partial label learning strategy is studied whichrefrains fromconducting disambiguation 本文研究了一...
Partial label learning deals with the problem where each training example is represented by a feature vector while associated with a set of candidate labels, among which only one label is valid. To learn from such ambiguous labeling information, the key is to try to disambiguate the candidate la...
Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unifi...
Sapp, B. Taskar Learning from partial labels J. Mach. Learn. Res., 12 (2011), pp. 1501-1536 View in ScopusGoogle Scholar [5] I. Couso, D. Dubois On the variability of the concept of variance for fuzzy random variables IEEE Trans. Fuzzy Syst., 17 (2009), pp. 1070-1080 View in...
Learning from Complementary Labels via Partial-Output Consistency Regularization In complementary-label learning (CLL), a multi-class classifier is learned from training instances each associated with complementary labels, which specify the classes that the instance does not belong to. Previous studies focu...
Partial label (PL) learning deals with training examples represented by a single instance associated with multiple candidate labels, among which only one ground-truth label resides [1], [30]. Different from the ordinary multi-class classification problems [18], [9], the supervision information is...
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heterosced... Q Guan,Q Chen,Y Huang - 《Algorithms》 被引量: 0发表: 2023年 Partial multi-label learning based on sparse asymmetric label correlation...