discriminative

简明释义

英[dɪsˈkrɪmɪnətɪv]美[dɪsˈkrɪməˌneɪtɪv]

adj. 区别的，歧视的；有识别力的

英英释义

单词用法

同义词

反义词

例句

1.The formulation of management policies and methods should cast away discriminative concept, safeguard the legitimate rights and interests of farmer workers, simplify procedures and reduce charges.

制定管理政策和办法，要摈弃歧视观念，维护农民工的合法权益，简化程序，减少收费。

2.Radar high resolution range profile provides potentially discriminative structure feature on the geometry of target, which has been used widely on radar target recognition.

雷达高分辨距离像包含目标的几何结构特征，已广泛应用于雷达目标识别领域。

3.The discriminative purchase policy in public purchase is of anti-competition by nature. It stops the resources from free flow therefore we should be against it.

公共采购中的歧视性采购政策在本质上是反竞争的，它阻碍了资源在全球范围内的自由流动，因此是我们应该加以反对的。

4.Assorted cooperation, integrating of loose and tight, discriminative supervising is the important orientation of the change of the regulation of press publishing.

将事业、产业属性单位分开，按“宣传导向型”、“社会公益型”、“商业运作型”分类运作、松紧结合、区别监管是新闻出版业规制变革的重要方向；

5.It is not discriminative to evaluate either a child's physical condition or his intellectual level.

评估一个孩子的身体状况或智力水平是没有区别的。

6.Predictions agreed well with observed outcome frequencies (adequate calibration) and showed a very good discriminative ability (AUC 0.85) in both data sets.

预测与观测结果频率（适当校准）相一致，并在两个数据集中呈现出很好的鉴别能力（AUC0.85 ）。

7.This is not to say that the procedure employed in studies of discriminative learning will be quite without effect.

这并不是说在判别学习的研究中所采用的方法将完全没有效果。

8.The study focused on discriminative 区分性 factors that affect consumer choices.

这项研究集中于影响消费者选择的区分性因素。

9.The discriminative 区分性的 features of the product help it stand out in a crowded market.

该产品的区分性的特性帮助它在竞争激烈的市场中脱颖而出。

10.In psychology, discriminative 辨别性 stimuli can influence behavior in significant ways.

在心理学中，辨别性刺激可以显著影响行为。

11.The machine learning model uses a discriminative 区分性的 approach to classify images more accurately.

这个机器学习模型使用一种区分性的方式来更准确地分类图像。

12.He has a discriminative 辨别力强的 taste in music, able to identify different genres easily.

他对音乐有辨别力强的品味，能够轻松识别不同的风格。

作文

In the field of machine learning, the concept of discriminative models plays a crucial role in understanding how algorithms can differentiate between various classes of data. A discriminative model is designed to model the decision boundary between different classes, rather than modeling the distribution of each class itself. This means that it focuses on the differences between classes, making it particularly effective for classification tasks. For example, when we consider the task of distinguishing between cats and dogs in images, a discriminative approach would learn to identify the specific features that set these two animals apart, such as their shape, fur texture, and color patterns.One of the most notable advantages of discriminative models is their efficiency in learning from data. By concentrating on the boundaries that separate different categories, these models typically require fewer data samples to achieve high accuracy compared to generative models, which attempt to model the entire distribution of the data. This efficiency makes discriminative models especially valuable in scenarios where labeled data is scarce or expensive to obtain.Moreover, discriminative models can be applied across various domains beyond image classification. In natural language processing, for instance, they are utilized in sentiment analysis to determine whether a piece of text conveys a positive or negative sentiment. Here, the model identifies specific words and phrases that are indicative of sentiment, allowing it to classify texts with remarkable precision. The ability to discern subtle differences in language reflects the power of discriminative techniques in extracting meaningful insights from complex datasets.However, it is essential to recognize that discriminative models are not without their limitations. While they excel at classification, they may struggle with generating new data points or understanding the underlying structure of the data. For instance, if tasked with generating realistic images of cats and dogs, a discriminative model would fall short, as its primary function is not to create but to classify. In contrast, generative models, which learn the distribution of data, can produce entirely new instances that resemble the training data, offering a different set of capabilities.In conclusion, the term discriminative refers to the ability to distinguish between different classes or categories effectively. In the context of machine learning, discriminative models are invaluable tools for classification tasks, enabling practitioners to leverage data efficiently and achieve high levels of accuracy. While they have their limitations, the focus on decision boundaries allows for powerful applications across various fields, from computer vision to natural language processing. Understanding the role of discriminative techniques is essential for anyone looking to deepen their knowledge of machine learning and its practical applications.

在机器学习领域，discriminative模型的概念在理解算法如何区分各种数据类别方面起着至关重要的作用。discriminative模型旨在建模不同类别之间的决策边界，而不是建模每个类别本身的分布。这意味着它关注类别之间的差异，使其在分类任务中特别有效。例如，当我们考虑区分猫和狗的图像时，discriminative方法将学习识别这两种动物之间的特定特征，如它们的形状、毛发纹理和颜色模式。discriminative模型最显著的优点之一是它们在从数据中学习时的高效性。通过集中于分隔不同类别的边界，这些模型通常需要比生成模型更少的数据样本来实现高准确率，后者试图对数据的整个分布进行建模。这种效率使得discriminative模型在标签数据稀缺或获取成本高昂的情况下尤其有价值。此外，discriminative模型可以应用于图像分类以外的各种领域。例如，在自然语言处理领域，它们被用于情感分析，以确定一段文本是传达积极还是消极的情感。在这里，模型识别出特定的单词和短语，这些单词和短语表明了情感，从而使其能够以惊人的精确度对文本进行分类。辨别语言中微妙差异的能力反映了discriminative技术在从复杂数据集中提取有意义见解方面的强大能力。然而，必须认识到discriminative模型并非没有局限性。虽然它们在分类方面表现出色，但在生成新数据点或理解数据的潜在结构时可能会遇到困难。例如，如果被要求生成逼真的猫和狗图像，discriminative模型将无法胜任，因为它的主要功能不是创建而是分类。相比之下，生成模型通过学习数据的分布，可以产生与训练数据相似的全新实例，提供了一组不同的能力。总之，discriminative这个术语指的是有效区分不同类别或类别的能力。在机器学习的背景下，discriminative模型是分类任务中不可或缺的工具，使从业者能够高效利用数据并达到高水平的准确性。尽管它们有其局限性，但对决策边界的关注使其在各个领域（从计算机视觉到自然语言处理）都能发挥强大的应用。理解discriminative技术的作用对于任何希望加深对机器学习及其实际应用知识的人来说都是至关重要的。