separability

简明释义

英[ˌseprəˈbɪləti]美[ˌseprəˈbɪləti]

n. [数] 可分性，分离性

英英释义

单词用法

同义词

反义词

例句

1.The experiment examines the linear separability of a pair of Chinese characters.

实验检验每一对汉字的线性可分性。

2.We have investigated the properties of entanglement by criterion of separability (non-entanglement) and equivalence of quantum states under local unitary transformations.

本论文从可分(非纠缠)性的判别及局域幺正变换下的不变量的等价类的角度来研究量子纠缠的性质。

3.And the sort separability criterion can also play the role of being a quantification measure on the characteristic extracting of flaw signals.

类别可分性判据对缺陷信号的特征提取也起了定量衡量尺度的作用。

4.The experiments show that it can be used to solve the separability problem.

实验表明，它可用于解决输入空间的可分性问题。

5.We investigate separability and entanglement measure for quantum states.

本学位论文研究了量子态的可分性和形成纠缠度。

6.The separability and separating conditions for mixed signals are analyzed in this paper. The limitation of nonlinear blind source separation methods is proposed.

该文分析了非线性混叠信号的可分离性及分离条件，指出现阶段非线性混叠信号盲分离的局限性。

7.The concept of separability is crucial in machine learning when dealing with different classes of data.

在处理不同类别数据时，可分离性的概念在机器学习中至关重要。

8.Researchers are studying the separability of different species in ecological systems to understand biodiversity.

研究人员正在研究生态系统中不同物种的可分离性以了解生物多样性。

9.In legal terms, the separability clause ensures that if one part of a contract is invalid, the rest remains enforceable.

在法律术语中，可分离性条款确保如果合同的一部分无效，其余部分仍然可强制执行。

10.In statistics, the separability of data points can indicate how well a model can classify them.

在统计学中，数据点的可分离性可以指示模型分类它们的效果。

11.The separability of concerns in software design helps to create more maintainable and scalable applications.

软件设计中关注点的可分离性有助于创建更易维护和可扩展的应用程序。

作文

In the realm of mathematics and computer science, the concept of separability plays a crucial role in understanding various theories and applications. At its core, separability refers to the ability to distinguish between different elements or components within a system. This idea can be applied to numerous fields, such as machine learning, data analysis, and even in everyday decision-making processes.For instance, in machine learning, the separability of data points is essential for the effectiveness of classification algorithms. When data points belonging to different categories are easily distinguishable from one another, it is said that the data is highly separable. This means that a model can effectively learn to classify new instances based on the patterns present in the training data. Conversely, if the data is not separable, the model may struggle to make accurate predictions, leading to poor performance.Consider a simple example involving two classes of data points represented in a two-dimensional space. If the points of one class cluster together on one side while the other class occupies a different region, we can easily draw a line (or hyperplane in higher dimensions) to separate them. This clear boundary indicates high separability. However, if the data points are intermixed, with no clear distinction between the two classes, the separability is low, complicating the classification task.Moreover, the importance of separability extends beyond just theoretical applications; it has practical implications in various industries. For example, in finance, investors analyze market trends and stock prices to identify which assets are performing well versus those that are underperforming. The ability to separate profitable investments from less favorable ones is crucial for making informed decisions. Here, separability becomes a guiding principle in portfolio management and risk assessment.In addition, separability is also significant in the field of psychology, particularly in understanding human behavior. Psychologists often study how individuals categorize their thoughts, emotions, and experiences. The process of separating these components allows for better comprehension and treatment of mental health issues. For instance, cognitive-behavioral therapy (CBT) encourages patients to differentiate between their thoughts and feelings, leading to improved emotional regulation and coping strategies.Furthermore, the concept of separability can be observed in our daily lives. When faced with complex decisions, individuals often break down the problem into smaller, more manageable parts. By separating the various factors that influence their choices, they can evaluate each aspect more clearly and arrive at a more rational conclusion. This practical application of separability showcases its relevance in enhancing critical thinking skills and decision-making abilities.In conclusion, the idea of separability is a multifaceted concept that transcends disciplinary boundaries. Whether in mathematics, finance, psychology, or daily decision-making, the ability to distinguish between different elements is vital for clarity and effectiveness. Understanding separability not only aids in theoretical exploration but also enriches practical applications, making it a fundamental principle in various fields. As we continue to navigate an increasingly complex world, honing our skills in recognizing and utilizing separability will undoubtedly enhance our analytical capabilities and overall understanding of the systems around us.

在数学和计算机科学领域，separability的概念在理解各种理论和应用中起着至关重要的作用。从本质上讲，separability指的是区分系统内不同元素或组件的能力。这个思想可以应用于许多领域，例如机器学习、数据分析，甚至日常决策过程。例如，在机器学习中，数据点的可分性对于分类算法的有效性至关重要。当属于不同类别的数据点彼此之间容易区分时，就称这些数据是高度可分离的。这意味着模型可以有效地学习根据训练数据中存在的模式来分类新实例。相反，如果数据不是可分离的，模型可能会难以做出准确预测，从而导致性能不佳。考虑一个简单的例子，涉及在二维空间中表示的两个类的数据点。如果一个类的点聚集在一侧，而另一个类占据不同区域，我们可以轻松地画一条线（或在更高维度中画超平面）来将它们分开。这条清晰的边界表明高可分离性。然而，如果数据点混合在一起，两个类之间没有明显的区别，那么可分离性就很低，这使得分类任务变得复杂。此外，separability的重要性不仅限于理论应用；它在各个行业中也具有实际意义。例如，在金融领域，投资者分析市场趋势和股票价格，以识别哪些资产表现良好，哪些表现不佳。将盈利投资与不太理想的投资分开的能力对于做出明智的决策至关重要。在这里，separability成为投资组合管理和风险评估中的指导原则。此外，separability在心理学领域也很重要，特别是在理解人类行为方面。心理学家经常研究个体如何对他们的思想、情感和经历进行分类。分离这些组件的过程有助于更好地理解和治疗心理健康问题。例如，认知行为疗法（CBT）鼓励患者区分他们的思想和情感，从而改善情绪调节和应对策略。此外，separability的概念也可以在我们的日常生活中观察到。当面临复杂决策时，个体通常会将问题分解为更小、更易管理的部分。通过分离影响他们选择的各种因素，他们可以更清晰地评估每个方面，并得出更理性的结论。这种对separability的实际应用展示了它在增强批判性思维技能和决策能力方面的相关性。总之，separability的思想是一个多面的概念，超越了学科的界限。无论是在数学、金融、心理学还是日常决策中，区分不同元素的能力对于清晰性和有效性至关重要。理解separability不仅有助于理论探索，还丰富了实际应用，使其成为各个领域的基本原则。随着我们继续在日益复杂的世界中航行，提高我们识别和利用separability的能力无疑将增强我们的分析能力和对周围系统的整体理解。