discriminative

"discriminative"的音标为[dɪˈskrimɪnətɪv] ，基本翻译为"有区别的，有歧视的"，速记技巧为：dis-前缀表示否定，-crimi-表示判断，-tive为形容词后缀。

英文词源：

Discriminative（判别性的）这个词来源于拉丁语中的“discriminare”（区分，辨别）。

变化形式：

名词形式：Discrimination（歧视）

形容词形式：Discriminatory（歧视的）

相关单词：

1. Difference（差异）：这个词与Discriminative有相似的含义，都表示区分和辨别。它来源于拉丁语中的“differtere”（使不同）。

2. Identify（识别）：这个词与Discriminative也有相似之处，都与辨别有关。它来源于拉丁语中的“identificare”（使相同）。

3. Distinction（区别）：这个词与Discriminative有直接的联系，表示区分和辨别。它来源于拉丁语中的“distinctus”（分开的）。

4. Discrimination（歧视）：这个词与Discriminative有相同的含义，表示对不同群体的偏见和不公平对待。它来源于拉丁语中的“dis”（不）和“criminare”（惩罚）。

5. Diversity（多样性）：这个词表示多种不同的特征或类型，也与判别性有关。它来源于拉丁语中的“diversus”（不同的）。

以上单词都与判别性有关，并且可以用来描述不同的事物或群体之间的差异和区别。这些单词在英语中广泛使用，并具有丰富的含义和用法。

常用短语：

1. discriminative training

2. positive discrimination

3. differential treatment

4. bias discrimination

5. target discrimination

6. signal discrimination

7. noise discrimination

8. feature discrimination

双语例句：

1. Discriminative training can help improve the performance of a machine learning model. (分类训练有助于提高机器学习模型的性能。)

2. Positive discrimination can be used to promote women and minority groups in the workplace. (积极歧视可用于在工作场所促进女性及少数群体的地位。)

3. Bias discrimination can lead to unfair treatment in society. (偏见歧视可能导致社会上的不公平待遇。)

4. Target discrimination is crucial for accurate image recognition. (目标识别中的目标区分对于准确图像识别至关重要。)

5. Signal discrimination is essential for signal processing systems to distinguish between noise and useful signals. (信号区分对于信号处理系统区分噪声和有用信号至关重要。)

6. Noise discrimination is necessary for audio equipment to filter out background noise. (噪声区分对于音频设备过滤背景噪声是必要的。)

7. Feature discrimination plays a vital role in machine learning algorithms to identify patterns and trends in data. (特征区分在机器学习算法中识别数据中的模式和趋势起着至关重要的作用。)

英文小作文：

Discrimination is a widespread problem in society, whether it be based on gender, race, or any other factor. However, positive discrimination and differential treatment can help mitigate some of these issues by promoting equality and fairness in society. At the same time, signal discrimination and noise discrimination are essential for effective signal processing systems and audio equipment, respectively. Similarly, feature discrimination plays a vital role in machine learning algorithms to identify patterns and trends in data. These examples show that discrimination is not always negative, but can also be used for positive purposes, provided it is implemented fairly and equitably. Therefore, we should strive to eliminate bias and promote fairness in all aspects of life, while also embracing the use of discrimination for beneficial purposes.