Active learningpartial label learningdistributed processingdisambiguation-free strategyActive learning(AL)trains a high-precision predictor model from small numbers of labeled data by iteratively annotating the
Semi-supervised partial label learning is an intersection of partial label learning [27], [11], [24] and semi-supervised learning [23]. It deals with partially labeled data along with unlabeled data [22]. The most common method for SPL problems is to fit off-the-shelf PL techniques to ...
To tackle this problem, we introduce a novel partial multi-label learning with noisy side information approach, which simultaneously removes noisy outliers from the training instances and trains robust partial multi-label classifier for unlabeled instances prediction. Specifically, we first represent the ...
including semi-supervised learning, multi-instance learning and multi-label learning, while the weak supervision scenario considered by partial label learning is different to those counterpart frameworks.
It is shown analytically and verified with both simulated and real data that the sequential version of the optimized PLSR is equivalent to PCA-based PLSR. 展开 关键词: Multivariate calibration, labeled data, unlabeled data 被引量: 1 年份: 2009 ...
The partial label ranking problem is an exciting learning scenario for non-standard supervised classification problems (e.g., multi-label). • The first transformation method is designed for the partial label ranking problem. • A pairwise algorithm combined with the resolution of the optimal bu...
Though existing VUDA methods enable the learning of transferable features across domains, they generally assume that the video source and target domains share an iden- tical label space, which may not hold in real-world ap- plications. With the presence of la...
The reason for it could be the multi-label profile of the Tox21 dataset. A more suitable algorithm would be developed for the multi-labeled datasets in our future work. In Fig. 3 we analyzed the prediction results of the model trained with or without balanced data. The upper two panels ...
algorithms in the context of learning from partly labeled data. In addition, the behavior of the algorithm is discussed and the relation between classes and clusters is investigated using a linear regression model. Finally, the complexity of the algorithm is briefly discussed....
20 papers with code • 5 benchmarks • 4 datasets Partial Domain Adaptation is a transfer learning paradigm, which manages to transfer relevant knowledge from a large-scale source domain to a small-scale target domain. Source: Deep Residual Correction Network for Partial Domain Adaptation ...