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Norm

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标签:class   通过   rom   需要   机器学习   center   second   sum   cond   

The unknow Word

The First Column The Second Column
leq less than or equal to
geq greater than or equal to
Euclidean norm [ju:klidien]欧几里得

2.5 Norms

Sometimes we need to measure the size of a vector. In machine learning, we usually measure the size of vectors using a function called a norm. Formally, the \(L^p\) norm is given by

有时我们需要衡量一个向量的大小。在机器学习中,我们经常使用被称为 范数(norm)的函数衡量向量大小。形式上,\(L^p\)范数定义如下:
\[||x||_p=(\sum_{i}|x_i|^p)^\frac{1}{p}\tag{2.30}\]
for \(\in \mathbb{R} , p\geq1\).

Norms, including the \(L^p\) norm, are functions mapping vectors to non-negativevalues. On an intuitive level, the norm of a vector x measures the distance fromthe origin to the point x. More rigorously, a norm is any functionfthat satisfies the following properties:

范数(包括 \(L^p\) 范数)是将向量映射到非负值的函数。直观上来说,向量 x 的范数衡量从原点到点 x 的距离。更严格地说,范数是满足下列性质的任意函数:

  • f(x)=0 => x=0
  • \(f(x+y)\leq f(x)+f(y)\) (the triangle inequality 三角不等式)
  • \(\bigvee \alpha \in \mathbb{R},f(\alpha x)=|\alpha|f(x)\)

The \(L^2\) norm,with p=2,is known as the Euclidean norm,which is simply the Euclidean distance from the origin to the point identified by x。The \(L^2\) norm is used so frequently in machine learning that it is often denoted simply as ||x||,with the subscript 2 omitted.It is also common to measure the size of a vector using the squared \(L^2\) norm,which can be calculated simply as \(x^T x\).

当 p = 2 时,\(L^2\)范数被称为 欧几里得范数( Euclidean norm)。它表示从原点出发到向量 x 确定的点的欧几里得距离。\(L^2\)范数在机器学习中出现地十分频繁,经常简化表示为 ||x||,略去了下标 2。平方 L2 范数也经常用来衡量向量的大小,可以简单地通过点积\(x^T x\) 计算。

Norm

标签:class   通过   rom   需要   机器学习   center   second   sum   cond   

原文地址:https://www.cnblogs.com/hugeng007/p/9534933.html

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