unsupervised

简明释义

英[ˌʌnˈsuːpərˌvaɪzd]美[ˌʌnˈsuːpərˌvaɪzd]

adj. 无人监督的；无人管理的

英英释义

单词用法

同义词

反义词

例句

1.An unsupervised approach would have the computer read billions of Web pages and sort the correctly spelled new terms from the typos.

非监督式学习方法将让计算机读取网页上无数个单词，拼写正确的新词从错误单词中筛选出来。

2.Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.

无监管学习的常见方法包括k - Means、分层集群和自组织地图。

3.Common sense and ability to work unsupervised.

在没有监督的情况下有能力并自觉工作。

4.I'll focus on the two most commonly used ones - supervised and unsupervised learning - because they are the main ones supported by Mahout.

我将重点讨论其中最常用的两个—监管和无监管学习—因为它们是Mahout支持的主要功能。

5.This is why you don't allow unsupervised babies near cat food.

这就是为什么你不会让小孩在没人管的时候靠近猫食的原因了。

6.Unsupervised learning, as you might guess, is tasked with making sense of data without any examples of what is correct or incorrect.

无监管学习的任务是发挥数据的意义，而不管数据的正确与否。

7.First of all, the significance of unsupervised word sense disambiguation study is introduced.

首先，介绍了无监督词义消歧研究的意义。

8.That's brilliant. Most of the kids will be sleeping unsupervised. Is that OK?

太好了。大多数孩子睡觉时没人照顾，可以吗？

9.Many AI algorithms can learn from data in an unsupervised 无监督的 manner.

许多AI算法可以以无监督的方式从数据中学习。

10.The experiment was conducted in an unsupervised 无人监督的 environment to test natural behaviors.

实验是在一个无人监督的环境中进行的，以测试自然行为。

11.In an unsupervised 无监督的 learning scenario, the model identifies patterns without labeled data.

在无监督的学习场景中，模型在没有标记数据的情况下识别模式。

12.Children should not be left unsupervised 无人看管的 in the park for their safety.

孩子们不应该在公园里无人看管的，以确保他们的安全。

13.Leaving pets unsupervised 无人看管的 can lead to accidents or damage to property.

把宠物无人看管的留着可能会导致事故或财产损失。

作文

In the realm of machine learning, there are various techniques that can be employed to analyze data. One of the most interesting approaches is called unsupervised learning. This method involves training a model on data without any labeled responses, meaning that the algorithm must work independently to identify patterns and relationships within the dataset. Unlike supervised learning, where the model is trained with input-output pairs, unsupervised learning allows for a more exploratory analysis of the data. The concept of unsupervised learning can be particularly beneficial in situations where acquiring labeled data is expensive or time-consuming. For instance, consider a scenario in which a company collects customer data but does not have the resources to label each entry. By applying unsupervised learning techniques, such as clustering or dimensionality reduction, the company can uncover hidden patterns in customer behavior without needing extensive labeling. This can lead to valuable insights that inform marketing strategies or product development. One common application of unsupervised learning is in customer segmentation. Businesses often want to categorize their customers into distinct groups based on purchasing behavior, preferences, or demographics. Using algorithms like K-means clustering, companies can automatically group customers into segments based on similarities in their data. This process is entirely unsupervised, as the algorithm identifies clusters without prior knowledge of the categories. As a result, businesses can tailor their marketing efforts to specific segments, enhancing customer engagement and satisfaction. Another area where unsupervised learning shines is in anomaly detection. In many industries, it is crucial to identify unusual patterns that may indicate fraud, equipment failure, or other significant events. By employing unsupervised techniques, such as isolation forests or one-class SVMs, organizations can detect anomalies in their data without needing labeled examples of what constitutes an anomaly. This proactive approach can save time and resources while improving overall operational efficiency. Despite its advantages, unsupervised learning also has its challenges. One major issue is the difficulty in evaluating the performance of unsupervised models, as there are no predefined labels to measure accuracy against. This can lead to ambiguity when interpreting results and necessitates a deeper understanding of the data and the chosen algorithms. Additionally, the risk of overfitting increases, as models may find spurious patterns that do not generalize well to new data. In conclusion, unsupervised learning offers a powerful set of tools for analyzing data without the need for labeled examples. Its applications in customer segmentation and anomaly detection demonstrate its potential to drive business insights and operational improvements. However, practitioners must remain aware of the inherent challenges and limitations associated with unsupervised methods. As the field of machine learning continues to evolve, the role of unsupervised learning will likely become even more prominent, paving the way for innovative solutions across various domains.

在机器学习领域，有多种技术可以用于分析数据。其中一种最有趣的方法称为无监督学习。这种方法涉及在没有任何标记响应的数据上训练模型，这意味着算法必须独立工作，以识别数据集中的模式和关系。与监督学习不同，监督学习是使用输入-输出对训练模型，无监督学习允许对数据进行更具探索性的分析。

无监督学习的概念在获取标记数据昂贵或耗时的情况下尤其有益。例如，考虑一个公司收集客户数据但没有资源标记每个条目的场景。通过应用无监督学习技术，如聚类或降维，公司可以在不需要大量标记的情况下揭示客户行为中的隐藏模式。这可以带来有价值的见解，从而为市场营销策略或产品开发提供信息。

无监督学习的一个常见应用是客户细分。企业通常希望根据购买行为、偏好或人口统计学将其客户分类。使用K均值聚类等算法，公司可以根据数据中的相似性自动将客户分组到不同的细分市场中。这个过程完全是无监督的，因为算法在没有先前知识的情况下识别出集群。因此，企业可以针对特定细分市场量身定制其营销工作，提高客户参与度和满意度。

无监督学习在异常检测方面也表现突出。在许多行业中，识别可能表示欺诈、设备故障或其他重大事件的异常模式至关重要。通过采用无监督技术，如孤立森林或一类SVM，组织可以在没有标记异常示例的情况下检测数据中的异常。这种主动的方法可以节省时间和资源，同时提高整体运营效率。

尽管有其优势，无监督学习也面临挑战。一个主要问题是评估无监督模型性能的困难，因为没有预定义的标签可以用来衡量准确性。这可能导致在解释结果时产生歧义，并需要对数据和所选算法有更深入的理解。此外，过拟合的风险增加，因为模型可能会找到不真实的模式，这些模式无法很好地推广到新数据上。

总之，无监督学习为在没有标记示例的情况下分析数据提供了一套强大的工具。它在客户细分和异常检测中的应用展示了其推动商业洞察力和运营改进的潜力。然而，实践者必须意识到与无监督方法相关的固有挑战和局限性。随着机器学习领域的不断发展，无监督学习的角色可能会变得更加突出，为各个领域的创新解决方案铺平道路。