文件名称:loo
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The leave-one-out cross-validation scheme is a method for estimating
％ the average generalization error. When calling
％ [Eloo,H] = loo(NetDef,W1,W2,PHI,Y,trparms) with trparms(1)>0, the network
％ will be retrained a maximum of trparms(1) iterations for each input-output
％ pair in the data set, starting from the initial weights (W1,W2). If
％ trparms(1)=0 an approximation to the loo-estimate based on "linear
％ unlearning" is produced. This is in general less accurate, but is much
％ faster to calculate.-The leave-one-out cross-validation scheme is a method for estimating the average generalization error. When calling [Eloo, H] = loo (NetDef, W1, W2, PHI, Y, trparms) with trparms (1)> 0, the network will be retrained a maximum of trparms (1) iterations for each input-output pair in the data set, starting from the initial weights (W1, W2). If trparms (1) = 0 an approximation to the loo-estimate based on linear unlearning is produced. This is in general less accurate, but is much faster to calculate.
％ the average generalization error. When calling
％ [Eloo,H] = loo(NetDef,W1,W2,PHI,Y,trparms) with trparms(1)>0, the network
％ will be retrained a maximum of trparms(1) iterations for each input-output
％ pair in the data set, starting from the initial weights (W1,W2). If
％ trparms(1)=0 an approximation to the loo-estimate based on "linear
％ unlearning" is produced. This is in general less accurate, but is much
％ faster to calculate.-The leave-one-out cross-validation scheme is a method for estimating the average generalization error. When calling [Eloo, H] = loo (NetDef, W1, W2, PHI, Y, trparms) with trparms (1)> 0, the network will be retrained a maximum of trparms (1) iterations for each input-output pair in the data set, starting from the initial weights (W1, W2). If trparms (1) = 0 an approximation to the loo-estimate based on linear unlearning is produced. This is in general less accurate, but is much faster to calculate.
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