Neural Network
Here let us study a supervised learning using a neural network. In a supervised learning, an conditional probability of an output y for a given input x is estimated.
Here let us study a supervised learning using a neural network. In a supervised learning, an conditional probability of an output y for a given input x is estimated.
bayes041.mp4Let an input x is in a 2-dimendional Euclidean space, and y in {0,1}. An unknown probability distribution q(y|x) is estimated by a neural network. In the movie, a network which is in a posterior distribution is displayed.
Let an input x is in a 2-dimendional Euclidean space, and y in {0,1}. An unknown probability distribution q(y|x) is estimated by a neural network. In the movie, a network which is in a posterior distribution is displayed.
bayes042.mp4An estimated posterior predictive distribution is displayed for each sample. The generalization error is far smaller than that by estimated using AIC. In a neural network, the essential dimension of a learning machine is far smaller than the dimension of the parameter. This is one of the main reason why deep learning has a better predictive performance.
An estimated posterior predictive distribution is displayed for each sample. The generalization error is far smaller than that by estimated using AIC. In a neural network, the essential dimension of a learning machine is far smaller than the dimension of the parameter. This is one of the main reason why deep learning has a better predictive performance.
A simple model selection procedure is introduced. 5 candidate models are compared according to the small generalization loss.
A simple model selection procedure is introduced. 5 candidate models are compared according to the small generalization loss.
Generalization loss can be estimated by WAIC and LOOCV. Note that these are smaller than estimated by AIC.
Generalization loss can be estimated by WAIC and LOOCV. Note that these are smaller than estimated by AIC.