APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Adding constraints to parameters, such as 1 and 2 norms
|
|
|
Expanding the training set, such as adding noise and transforming data
|
|
|
Dropout
|
|
|
Early stopping
|
Detailed explanation-1: -Overfitting, early stopping is one of methods used to prevent overfitting. Generalization error. Regularization (mathematics) Statistical learning theory. Boosting (machine learning) Cross-validation, in particular using a “validation set” Neural networks.
Detailed explanation-2: -Regularization is the most popular technique to prevent overfitting. It is a group of methods that forces the learning algorithms to make a model simpler. Applying the regularization technique may slightly increase the bias but slightly reduces the variance.
Detailed explanation-3: -Simplifying The Model. The first step when dealing with overfitting is to decrease the complexity of the model. Early Stopping. Use Data Augmentation. Use Regularization. Use Dropouts.