Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive GAUSS-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
標簽：
recursive
prediction
algorithm
GAUSS-Ne
上傳時間：
2016-12-27
上傳用戶：ljt101007
