sigmoid

简明释义

英[ˈsɪɡmɒɪd]美[ˈsɪɡˌmɔɪd]

adj. 乙状结肠的; C 形的; S 形的

n. 乙状结肠（等于 sigmoidal）; S 状弯曲

英英释义

单词用法

同义词

反义词

例句

1.Conclusions: Focus of the disease is situated in rectum and sigmoid colon.

结论：溃疡性结肠炎病变主要位于直肠和乙状结肠。

2.The authors reported nursing of 12 patients with sigmoid stoma undergoing radiotherapy after rectal cancer surgery.

笔者报道了12例直肠癌乙状结肠造口术后放射治疗患者的护理。

3.Cancer refers to the teeth straight line-at the junction between the sigmoid colon cancer, digestive tract is one of the most common malignant tumor.

直肠癌是指齿线至直——乙状结肠交界处之间的癌肿，是消化道最常见的恶性肿瘤之一。

4.Boll shell percent age resembled a sigmoid curve with flowering stage.

铃壳率变化与开花期呈二次曲线。

5.The adenomas in sigmoid colon were most common and the adenocarcinomas in rectum were most common.

大肠散发性腺瘤以乙状结肠最多见，而结肠腺癌则在直肠最常见。

6.But the flatus tube, used to treat a condition of the sigmoid colon, is one no surgeon relishes.

但用于治疗S形结肠问题的肠管对外科医生来说，也并非视觉享受啊。

7.Dislodging entrapped fecal matter above the sigmoid colon area.

取出包埋粪便以上乙状结肠区。

8.Of or relating to the sigmoid flexure in the large intestine.

属于或关于大肠内乙状结肠。

9.The sigmoid colon and upper rectum were pressed backward and narrowed on colic photography.

结肠气钡双重造影：乙状结肠及直肠上段向后受压变细。

10.The output of the neural network is transformed using a sigmoid function to produce probabilities.

神经网络的输出通过一个sigmoid函数转换，以产生概率。

11.The sigmoid function is particularly useful in models where a probability interpretation is required.

在需要概率解释的模型中，sigmoid函数特别有用。

12.To ensure smooth gradients during training, we apply the sigmoid function.

为了确保训练过程中的平滑梯度，我们应用sigmoid函数。

13.In logistic regression, we often use the sigmoid curve to model binary outcomes.

在逻辑回归中，我们通常使用sigmoid曲线来建模二元结果。

14.The sigmoid activation function helps in squashing the output to a range between 0 and 1.

在激活函数中，sigmoid帮助将输出压缩到0和1之间。

作文

The term sigmoid refers to a specific mathematical function that is often used in various fields, including statistics, machine learning, and biology. The sigmoid function has an S-shaped curve, which makes it particularly useful for modeling situations where there are two possible outcomes, such as yes or no, pass or fail, or success or failure. This characteristic of the sigmoid function allows it to effectively compress input values into a range between 0 and 1, making it ideal for probabilistic interpretations.In machine learning, one of the most common applications of the sigmoid function is in logistic regression. Logistic regression is a statistical method used for binary classification problems. By applying the sigmoid function to the output of a linear equation, we can transform any real-valued number into a value that lies between 0 and 1, which can then be interpreted as a probability. For instance, if the output of the linear equation is 2, applying the sigmoid function yields a value close to 0.88, indicating a high likelihood of belonging to the positive class.The mathematical representation of the sigmoid function is given by the formula: f(x) = 1 / (1 + e^(-x)), where e is the base of the natural logarithm. This formula highlights how the sigmoid function approaches 0 as x approaches negative infinity and approaches 1 as x approaches positive infinity. The steepness of the curve can be adjusted using a scaling factor, which can be particularly useful in fine-tuning models in machine learning.Beyond its applications in machine learning, the sigmoid function also appears in various biological contexts. For example, it can describe population growth where the initial growth is exponential, but as resources become limited, the growth rate slows down and eventually stabilizes at a carrying capacity. This behavior is often modeled using a sigmoid curve, demonstrating its versatility across different disciplines.Furthermore, understanding the sigmoid function is crucial when working with neural networks. In neural networks, activation functions play a significant role in determining the output of neurons. The sigmoid function is one of the traditional activation functions, although it has been largely replaced by other functions like ReLU (Rectified Linear Unit) in modern architectures due to issues like vanishing gradients. However, the sigmoid function is still relevant, especially in the output layer of binary classification tasks, where it maps the output to a probability.In conclusion, the sigmoid function is a fundamental concept that bridges mathematics, machine learning, and biology. Its ability to model binary outcomes and compress values into a manageable range makes it invaluable in many applications. By understanding the properties and implications of the sigmoid function, one can gain deeper insights into various phenomena, whether they are related to data science, biological systems, or statistical analysis. Mastering the sigmoid function is essential for anyone looking to excel in fields that rely on quantitative analysis and predictive modeling.

术语sigmoid指的是一种特定的数学函数，广泛应用于统计学、机器学习和生物学等多个领域。sigmoid函数具有S形曲线，这使得它在建模存在两个可能结果的情况时特别有用，例如是或否、通过或未通过、成功或失败。sigmoid函数的这一特性使其能够有效地将输入值压缩到0到1之间的范围，非常适合概率解释。在机器学习中，sigmoid函数最常见的应用之一是在逻辑回归中。逻辑回归是一种用于二元分类问题的统计方法。通过将sigmoid函数应用于线性方程的输出，我们可以将任何实数值转换为一个位于0和1之间的值，然后可以将其解释为概率。例如，如果线性方程的输出为2，应用sigmoid函数会得到一个接近0.88的值，表明属于正类的可能性很高。sigmoid函数的数学表示为：f(x) = 1 / (1 + e^(-x))，其中e是自然对数的底数。这个公式突显了sigmoid函数如何在x接近负无穷时趋近于0，而在x接近正无穷时趋近于1。曲线的陡峭程度可以通过缩放因子进行调整，这在微调机器学习模型时特别有用。除了在机器学习中的应用外，sigmoid函数还出现在各种生物学背景下。例如，它可以描述人口增长，其中初期增长是指数级的，但随着资源的限制，增长率减缓并最终稳定在承载能力。这种行为通常使用sigmoid曲线建模，展示了其在不同学科中的多功能性。此外，理解sigmoid函数在处理神经网络时至关重要。在神经网络中，激活函数在确定神经元的输出方面起着重要作用。sigmoid函数是传统的激活函数之一，尽管由于梯度消失等问题，它在现代架构中已被其他函数（如ReLU）大多取代。然而，sigmoid函数仍然相关，尤其是在二元分类任务的输出层中，它将输出映射到概率。总之，sigmoid函数是一个基本概念，连接了数学、机器学习和生物学。它建模二元结果和将值压缩到可管理范围的能力使其在许多应用中不可或缺。通过理解sigmoid函数的属性和含义，人们可以深入洞察各种现象，无论这些现象与数据科学、生物系统还是统计分析有关。掌握sigmoid函数对于希望在依赖定量分析和预测建模的领域中脱颖而出的人来说至关重要。