Pattern Classification

where η is the learning rate which indicates the relativesize of the change in weightsw(m +1) = w(m) + ∆w(m)where m is the m-th training pattern presented26• Error on the hidden–to-output weights∂J∂wkj=∂J∂netwhere the sensitivity of unit k is defined as:k∂net.∂wand describes how the overall error changes with theactivation of the unit’s net activation∂J∂J∂zkδk= − = − . = ( tk− zk) f' ( netk)∂net∂z∂netkkkjkk= −δk∂net∂wδkjkk∂J= −∂netkPattern Classification, Chapter 6