Soft intervalIt is a method introduced to deal with linear inseparable problems and reduce the influence of noise. It sacrifices the restriction that certain points must be correctly divided in exchange for a larger segmentation interval. Its characteristic is that there will be error points during classification for the overall effect.

Soft and hard intervals

Hard margin classification method: It has a hard requirement that all sample points meet one point, that is, the distance between classification planes is greater than a certain value.

Soft margin classification method: allows individual samples not to meet the constraints, thereby removing some noise or dealing with problems that cannot be classified by hard margins.

Application of soft margin

If the data mapping is still linearly inseparable in high dimensions, then the hyperplane needs to be adjusted, that is, the soft interval; if the data is linearly separable, but there may still be noise in the data, the soft interval can be used to reduce the impact of the noise.

Related terms: hard margin, soft margin maximization