In the proposed loss function, we combine the classifier predictions, based on the labeled data, and the pairwise similarity between labeled and unlabeled examples. The main goal of the proposed loss function is to minimize the inconsistency between classifier predictions and the pairwise similarity....
Labeled data is raw data that has been assigned labels to add context or meaning, which is used to train machine learning models in supervised learning.
Combining labeled and unlabeled data with co-training:(与co-training结合标记和未标记数据).pdf,Combining Lab eled and Unlab eled Data with CoTraining y Avrim Blum Tom Mitchell School of Computer Science School of Computer Science Carnegie Mellon Univer
Some companies, such as Drive, are using deep learning to enhance automation for annotating data, as a way to accelerate the tedious process of data labelling. Let’s use unlabeled data Koopman, however, believes there is another way to “squeeze the value out of the accumulated data.” How...
Combining_labeled_and_unlabeled_data_with_co-training 下载积分: 3000 内容提示: Combining Lab eled and Unlab eled Data with Co-Training? yAvrim BlumScho ol of Computer ScienceCarnegie Mellon UniversityPittsburgh, PA 15213-3891avrim+@cs.cmu.eduTom MitchellScho ol of Computer ScienceCarnegie Mellon...
Then, the estimated class membership probabilities are used to label and weight unlabeled instances. At last, a naive Bayes is trained again using both the originally labeled data and the (newly labeled and weighted) unlabeled data. Our experimental results based on a large number of UCI data ...
Zhu X. and Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, 2002. 概 本文通过将有标签数据传播给无标签数据
labeled data can help us learn the distribution over ob- ject descriptions. Links among the unlabeled data (or test set) can pro- vide information that can help with classification. Links between the labeled training data and unlabeled ...
We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to signi cant improvement of hypotheses in practice. As part of our analysis, we provide new re- 展开 关键词: CiteSeerX citations Combining labeled and unlabeled data with co-...
Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. ...