Semi-supervised clustering algorithmshared nearest neighborsfuzzy-rough setdefect type recognition systemcross-linked polyethyleneIn this study, a semi-supervised learning algorithm for data classification and defect type recognition system for a 25 kVcross-linked polyethylene(XLPE) underground power cable ...
1. 算法示意图 (Algorithm Schematic) 图1 CA-SSL框架示意图 图2 CA-SSL与自监督和半监督方法的对比 2. 解决什么样的问题? (Motivation) 已有的自监督和半监督方法会从训练数据中提取要么与任务过于不相关的(task-unrelated)或者要么太特定于任务的(task-specific)训练信号,这在任务特定性(task specificity)上导...
但如果把unlabeled data 也考虑进去,我们可能会根据unlabeled data 的分布,分界线画成图中的斜线; semi-supervised learning使用unlabel的方式往往伴随着一些假设,学习有没有用,取决于你这个假设合不合理。(比如灰色的点也可能是个狗不过背景跟猫照片比较像) 2、Semi-Surpervised在生成模型中如何实现的(EM算法) 回顾...
30 Semi-Supervised Learning Algorithms. Contribute to YGZWQZD/LAMDA-SSL development by creating an account on GitHub.
With a minimal amount of labeled data and plenty of unlabeled data, semi-supervised learning shows promising results in classification tasks while leaving the doors open for other ML tasks. Basically, the approach can make use of pretty much any supervised algorithm with some modifications needed. ...
observations is that semi-supervised learning should not be seen as a guaranteed way of achieving improved prediction performance by the mere introduction of unlabelled data. Rather, it should be treated as another direction in the process of finding and configuring a learning algorithm for the task...
The most common types of semi-supervised learning are: Self-training: With self-training, the process uses the labeled data set to train the algorithm, then subsequent training generates high-confidence (more than 99% probability) pseudo-labels for the unlabeled data set such that all records ha...
A semi-supervised learning algorithm will have the 250 labeled rows as well as the 250 unlabeled rows that could be used in numerous ways to improve the labeled training dataset.Next, we can establish a baseline in performance on the semi-supervised learning dataset using a supervised learning ...
regions, they belong to the same class cluster [28]. According to this assumption, the decision boundary should not cross high-density areas but instead lie in low-density regions [35]. Therefore, the learning algorithm can use a large amount of unlabeled data to adjust the classification ...
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