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
Label propagation is a semi-supervised technique that makes use of the labeled and unlabeled data to learn about the unlabeled data. Quite often, data that will benefit from a classification algorithm is difficult to label. For example, labeling data might be very expensive, so only a subset i...
In accordance with an embodiment of the invention, there is provided a technique for permitting a machine to discover classes and topics that data contains and to annotate data objects with those identified classes. The technique enables machines to group and annotate data objects in ways that are...
LabelPropagation出自论文“Learning from Labeled and Unlabeled Data with Label Propagation”,目前已在gtrick中实现: gtrick/label_prop.py at main · sangyx/gtrick (github.com)github.com/sangyx/gtrick/blob/main/gtrick/dgl/label_prop.py 关于gtrick的介绍: gtrick: GNN的trick工具箱144 赞同 ·...
4.3、 Further Analysis and Ablation Studies 通过选取较新的SSL(Semi-Supervise Learning)model,证实unlabeled data能够超越baselines. 5、A Closer Look at Unlabeled Data under Class Imbalance 根据上述实验以及结论,进一步挖掘SSL的性能,其能否在实际的Imbalanced data中表现出较好的性能呢?对于balanced data,SSL往往...
Use both labeled and unlabeled data to train a SemiSupervisedSelfTrainingModel object. Label new data using the trained model. Randomly generate 15 observations of labeled data, with 5 observations in each of three classes. Get rng('default') % For reproducibility labeledX = [randn(5,2)*0.2...
For learning, it uses unlabeled data, which is basically raw data that can be found “in the wild” and is usually unstructured and unprocessed. Naturally, unsupervised machine learning algorithms have a lot of limitations. As they don’t have any starting point for their training, there are ...
unlabeledX = [randn(100,2)*0.25 + ones(100,2); randn(100,2)*0.25 - ones(100,2); randn(100,2)*0.5]; Fit labels to the unlabeled data by using a semi-supervised graph-based method. Specify label spreading as the labeling algorithm, and use an automatically selected kernel scale facto...
fitsemiself creates a semi-supervised self-training model given labeled data, labels, and unlabeled data.
Paper tables with annotated results for Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation