Each record of the base is an indexing record, including cross concordance of class numbers and strings. It can be used to realize text categorization through computing the similarity between feature strings of unlabeled documents with each indexing examples. Empirical results prove that this method ...
It makes use of unlabeled data (typically a large amount) for training, besides a small amount of labeled data. Semi-supervised learning is applied in cases where it's expensive to acquire a fully labeled dataset and more practical to label a small subset. For example, it often requires ...
Existing a lot of unlabeled data and few labeled data is one of the most common problems in real datasets. Semi-supervised classification methods can well handle such a problem and have a desirable performance. Among them, one of the most successful methods in dealing with shortage of labeled ...
unlabeled data, but in practice, we just assume theUclass is a noisy negative class that actually contains some positive examples. Thus, the goal of PU learning is to (1) estimate the proportion of negatively labeled examples that actually belong to the positive class (seefraction\_noise\_in...
From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Image Classification is an end-to-end example that starts with an unlabeled dataset, labels it using the Ground Truth API, analyzes the results, trains an image classification neural net using the...
Label propagation is frequently encountered in machine learning and data mining applications on graphs, either as a standalone problem or as part of node c
(2001). Discovering outlier filtering rules from unlabeled data. In Proc. KDD (pp. 389–394). Yamanishi, K., Takeuchi, J., Williams, G., & Milne, P. (2000). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. In Proc. KDD (pp. 250–254)...
showed that faster clearance of radioactivity from the bloodstream was observed with the initial injection of unlabeled Rituximab (250 μg m-2) due to the blocking by cold Rituximab of positive CD-20 binding sites on B-lymphocytes in the circulation and spleen.36 More information describing our...
Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, 2007. 759--766. Google Scholar [2] Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In: Proceedings of the 27th ...
We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time. Particularly, no examples, labeled or unlabeled, or any other knowledge ...