## 求解最小二乘 前面说了，对regression问题最小风险泛函（risk functional）的结果是 现在我们对$f(x)$做线性参数假设，然后用最小二乘来求解参数，就有 其中是一个的矩阵，p是维数。
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Empirical risk functional 经验风险泛函
fuzzy risk functional 模糊风险泛函
rough risk functional 粗糙风险泛函
expected risk functional 期望风险泛函
the empirical risk functional 期望风险泛函
fuzzy expected risk functional 模糊期望风险泛函
rough empirical risk functional 粗糙经验风险泛函
fuzzy empirical risk functional 模糊经验风险泛函
Functional Risk 功能风险
The extracted support vectors can be trained instead of total data set, so computational cost will be reduced. (2) The loss function is very important to risk functional of the SVM.
损失函数是支持向量机风险泛函的关键部分，选择不同的损失函数可以构造不同类型的支持向量机。
This best practice allows you to prioritize test cases based on their complexity, risk, and functional area, which will create a quality estimate about how long the testing will take.
这种最佳的操作允许您基于测试用例的复杂性、风险以及功能性区域来安排它们的优先级，来建立关于测试将要持续多久的质量估计。
Too much separation results in functional decomposition and its associated integration issues; too much emphasis on integration and you risk missing important functional issues.
分离太多会导致功能分解和相关的集成问题，太过强调集成，您会有错过重要功能问题的危险。
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