Results 231 to 240 of about 195,059 (253)

Instance-dependent analysis of learning algorithms

2017
On the one hand, theoretical analyses of machine learning algorithms are typically performed based on various probabilistic assumptions about the data. While these probabilistic assumptions are important in the analyses, it is debatable whether such assumptions actually hold in practice.
openaire   +1 more source

Confidence-Based PU Learning With Instance-Dependent Label Noise

IEEE Transactions on Neural Networks and Learning Systems
Positive and unlabeled (PU) learning, which trains binary classifiers using only PU data, has gained vast attentions in recent years. Traditional PU learning often assumes that all the positive samples are labeled accurately. Nevertheless, due to the reasons such as sample ambiguity and insufficient algorithms, label noise is almost unavoidable in this
Hong Tao, Chenping Hou, Xiaoyu Ma
exaly   +3 more sources

A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels

IEEE Transactions on Pattern Analysis and Machine Intelligence
Many machine learning algorithms are known to be fragile on simple instance-independent noisy labels. However, noisy labels in real-world data are more devastating since they are produced by more complicated mechanisms in an instance-dependent manner.
Yuxuan Du, Jun Yu, Bo Han
exaly   +3 more sources

Probabilistic instance dependent label refinement for noisy label learning

Machine Learning
Hao-Yuan He 0001, Yu Liu 0083, Renbiao Liu, Zheng Xie 0001, Ming Li 0005   +4 more
exaly   +2 more sources

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Partial label learning (PLL) refers to the classification task where each training instance is ambiguously annotated with a set of candidate labels. Despite substantial advancements in tackling this challenge, limited attention has been devoted to a more specific and realistic setting, denoted as instance-dependent partial label learning (IDPLL ...
Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang   +2 more
openaire   +1 more source

Improving the Instance-Dependent Transition Matrix Estimation by Exploiting Self-Supervised Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
The transition matrix reveals the transition relationship between clean labels and noisy labels. It plays an important role in building statistically consistent classifiers for learning with noisy labels. However, in real-world applications, the transition matrix is usually unknown and has to be estimated.
Jun Yu, Yexiong Lin, Bo Han
exaly   +3 more sources

Instance-Dependent Positive-Unlabelled Learning

2018
An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant importance in both academia and industry. This thesis addresses the PU learning problem following a natural strategy that treats unlabelled data as negative.
openaire   +1 more source

A recent survey on instance-dependent positive and unlabeled learning

Fundamental Research, 2022
Muhammad Imran Zulfiqar, Chen Gong
exaly  

Instance-dependent cost-sensitive learning: do we really need it?

Annual Hawaii International Conference on System Sciences, Proceedings of the, 2022
Bart Baesens, Wouter Verbeke, Tim Verdonck   +2 more
exaly  

Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels

Lecture Notes in Computer Science, 2022
Yipeng Qin, Guanbin Li, Feng Liu
exaly