深度学习中的inductive bias解释

深度学习中的inductive bias解释"/>

深度学习中的inductive bias解释

机器学习算法在学习过程中对某种假设（hypothesis）的偏好，称为“归纳偏好”（inductive bias），或简称为“偏好”。

所谓的inductive bias，指的是人类对世界的先验知识，对应在网络中就是网络结构。

下面是一些inductive bias的例子：

Algorithm | Inductive Bias

Linear Regression | The relationship between the attributes x and the output y is linear. The goal is to minimize the sum of squared errors.

Single-Unit Perceptron | Each input votes independently toward the final classification (interactions between inputs are not possible).

Neural Networks with Backpropagation | Smooth interpolation between data points.

K-Nearest Neighbors | The classification of an instance x will be most similar to the classification of other instances that are nearby in Euclidean distance.

Support Vector Machines | Distinct classes tend to be separated by wide margins.

Naive Bayes | Each input depends only on the output class or label; the inputs are independent from each other.