nearest neighbor classifier

Bayesian classifier

linear classifier

Support vector machine classifier

neural network classifier

random forest

Adaboost

0-1 loss

Multi-class support vector machine loss

cross entropy loss

L1 loss

L2 loss

first order method

second order method

Data set partitioning

Data preprocessing

data augmentation

Underfitting and overfitting

Hyperparameter tuning

Model integration

is a linear mapping that maps input image features to category scores

The trained average is similar to the category after visualization

Considered as a template, the higher the weight of the input image and the template, the more similar it is.

The line with a score equal to 0 is the decision surface

A bridge between model performance and model parameters, guiding model parameter optimization

is a function that measures the degree of inconsistency between the predicted value of a given classifier and the true value, and its output is usually a non-negative real value

The non-negative real value output can be used as a feedback signal to adjust the classifier parameters to reduce the loss value corresponding to the current instance and improve the classification effect.

Multi-class support vector machine loss

regular term

gradient descent

stochastic gradient descent

Mini-batch stochastic gradient descent algorithm

Classic division

k-fold cross validation

Remove the mean

Normalized

decorrelate

albino