ML&DL：ML&DL相關概念的原始英文解釋——對理解最初的ML和DL的相關概念的定義非常有用

ML分類概念

1、Parametric and Nonparametric Algorithms

What is a parametric machine learning algorithm and how is it different from a nonparametric machine learning algorithm?

1、Assumptions can greatly simplify the learning process, but can also limit what can be learned. Algorithms that simplify the function to a known form are called parametric machine learning algorithms.
(1)、The algorithms involve two steps:
A、Select a form for the function.
B、Learn the coefficients for the function from the training data.
Some examples of parametric machine learning algorithms are Linear Regression and Logistic Regression.

2、Algorithms that do not make strong assumptions about the form of the mapping function are called nonparametric machine learning algorithms. By not making assumptions, they are free to learn any functional form from the training data.
(1)、Non-parametric methods are often more flexible, achieve better accuracy but require a lot more data and training time.
(2)、Examples of nonparametric algorithms include Support Vector Machines, Neural Networks and Decision Trees.

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