unlabeled

简明释义

英[ʌnˈleɪbld]美[ʌnˈleɪbəld]

无标号的

未标记的

未贴标签的

英英释义

单词用法

同义词

反义词

例句

1.But I agree with him that “unlabeled, it’s a fun test of your knowledge of nations , can you guess them all?”

不过他希望不公布这些国名，我同意他这个想法。这是一项考察你对世界各国知识的有趣测试，你能全部猜中吗？

2.In the process, we calculate the posterior probability of semantics by unlabeled samples information.

在计算的过程中，使用了未标记样本的信息计算语义出现的后验概率。

3.Due to the supervised view of point, most of the present tensor dimensionality reduction methods cannot take full advantage of the unlabeled data.

现有的张量维数约简方法大都是监督的，它们不能有效利用未标签样本数据的信息。

4.In this paper, we study the problem of building a text classifier from a keyword and unlabeled documents, so as to avoid labeling documents manually.

为了减少人工标记样本的代价，本文提出了通过关键词和未标记样本建立分类器的新方法。

5.As we all know, obtaining a lot of labeling samples is a time-consuming and laborious work, but obtaining large number of unlabeled samples is a very easy task.

众所周知，获取大量已标记样本是一项费时费力的工作，而获取大量未标记样本却是一项非常容易的工作。

6.There was no significant difference between the results of Trypan blue staining in labeled and unlabeled cells.

标记细胞与未标记细胞的台盼兰染色结果无显著差异。

7.In machine learning, training with unlabeled 未标记的 data can be challenging.

在机器学习中，使用未标记的数据进行训练可能会很具挑战性。

8.She decided to organize her unlabeled 未标记的 files for better clarity.

她决定整理她的未标记的文件以便更清晰。

9.The researcher found an unlabeled 未标记的 jar in the lab.

研究人员在实验室里发现了一个未标记的瓶子。

10.The unlabeled 未标记的 products on the shelf confused the customers.

货架上的未标记的产品让顾客感到困惑。

11.The unlabeled 未标记的 boxes made it difficult to find the right supplies.

这些未标记的箱子使得找到正确的用品变得困难。

作文

In the world of data science and machine learning, the term unlabeled refers to data that has not been assigned any specific category or class. This type of data can be quite common, especially in situations where collecting labeled data is either too expensive or impractical. For instance, imagine a scenario where a researcher is trying to develop an algorithm to identify different species of birds from images. If they only have a few images that are labeled with the correct species names, they would face a significant challenge when trying to train their model effectively. The majority of their dataset may be unlabeled, making it difficult to teach the algorithm what to look for in the images.The use of unlabeled data presents both challenges and opportunities. On one hand, training models on unlabeled data can lead to less accurate outcomes since the model does not have clear guidance on what features to learn. However, researchers have developed various techniques to leverage unlabeled data effectively. One popular method is called unsupervised learning, where algorithms attempt to find patterns and structures within the unlabeled data itself without any prior labels. This approach can uncover hidden insights that might not be apparent with labeled datasets.Another interesting aspect of unlabeled data is its role in semi-supervised learning. In this scenario, a small amount of labeled data is used alongside a larger set of unlabeled data. By combining these two types of data, the model can improve its performance significantly. For example, if a company wants to classify customer feedback into positive or negative sentiments, they might have a limited number of reviews that are labeled. The rest of the reviews could be unlabeled. By using both labeled and unlabeled data, the algorithm can learn more robust features and generalize better to new, unseen data.Moreover, the concept of unlabeled data extends beyond just machine learning. In everyday life, we encounter many situations where information is unlabeled. For instance, consider a box of assorted items that do not have any labels. Without knowing what each item is, it can be challenging to find what you need. Similarly, in research, scientists often deal with unlabeled samples that require careful analysis before they can draw conclusions. In these cases, understanding the context and applying analytical skills become crucial.In conclusion, while unlabeled data poses certain challenges in the fields of data science and machine learning, it also offers unique opportunities for discovery and innovation. By employing techniques such as unsupervised and semi-supervised learning, researchers can harness the power of unlabeled data to extract valuable insights and improve their models. As we continue to generate vast amounts of data daily, the ability to work with unlabeled datasets will become increasingly important, paving the way for advancements in technology and our understanding of complex systems.

在数据科学和机器学习的世界中，术语unlabeled指的是尚未被分配任何特定类别或类的数据。这种类型的数据相当常见，尤其是在收集标记数据既昂贵又不切实际的情况下。例如，设想一个场景，研究人员试图开发一种算法来识别来自图像的不同鸟类。如果他们只有少量标记正确物种名称的图像，他们在有效训练模型时将面临重大挑战。他们的数据集中的大多数可能是unlabeled，这使得教算法在图像中寻找什么变得困难。使用unlabeled数据既带来了挑战，也带来了机会。一方面，在unlabeled数据上训练模型可能导致结果不够准确，因为模型没有明确的指导去学习哪些特征。然而，研究人员已经开发出多种技术来有效利用unlabeled数据。一种流行的方法称为无监督学习，其中算法试图在unlabeled数据中找到模式和结构，而无需任何先前的标签。这种方法可以揭示隐藏的洞察，这在使用标记数据集时可能并不明显。Unlabeled数据的另一个有趣方面是其在半监督学习中的作用。在这种情况下，一小部分标记数据与大量unlabeled数据一起使用。通过结合这两种类型的数据，模型的性能可以显著提高。例如，如果一家公司想将客户反馈分类为积极或消极情绪，他们可能只有有限数量的已标记评论。其余的评论可能是unlabeled。通过同时使用标记和unlabeled数据，算法可以学习更强大的特征，并更好地泛化到新的、未见过的数据。此外，unlabeled数据的概念超出了机器学习的范围。在日常生活中，我们遇到许多信息unlabeled的情况。例如，考虑一个没有任何标签的杂项物品箱子。如果不知道每个物品是什么，找到所需的东西可能会很具挑战性。同样，在研究中，科学家们经常处理需要仔细分析的unlabeled样本，然后才能得出结论。在这些情况下，理解上下文和应用分析技能变得至关重要。总之，虽然unlabeled数据在数据科学和机器学习领域带来了某些挑战，但它也提供了发现和创新的独特机会。通过采用无监督学习和半监督学习等技术，研究人员可以利用unlabeled数据的力量提取有价值的洞察并改善他们的模型。随着我们每天生成大量数据，处理unlabeled数据集的能力将变得越来越重要，为技术进步和我们对复杂系统的理解铺平道路。