"\u0160\u00EDma, Ji\u0159\u00ED" . "92;105" . "14"^^ . . "Berlin" . "Washington [US]" . . . . "687067" . . . "Minimizing the Quadratic Training Error of a Sigmoid Neuron is Hard."@en . . . . "0"^^ . "[2836B0B38739]" . "1"^^ . "0"^^ . . "2001-11-25+01:00"^^ . "1"^^ . "RIV/67985807:_____/01:06010079!RIV/2003/AV0/A06003/N" . . "Algorithmic Learning Theory." . . . "3-540-42875-5" . "Minimizing the Quadratic Training Error of a Sigmoid Neuron is Hard."@en . "We first present a brief survey of hardness results for training feedforward neural networks. These results are then completed by the proof that the simplest architecture containing only a single neuron that applies the standard (logistic) activation function to the weighted sum of n inputs is hard to train. In particular,the problem of finding the weights of such a unit that minimize the relative quadratic training error within 1 or its average (over a training set) within 13/(31n) of its infimum proves..."@en . . . "RIV/67985807:_____/01:06010079" . . . "Minimizing the Quadratic Training Error of a Sigmoid Neuron is Hard." . "We first present a brief survey of hardness results for training feedforward neural networks. These results are then completed by the proof that the simplest architecture containing only a single neuron that applies the standard (logistic) activation function to the weighted sum of n inputs is hard to train. In particular,the problem of finding the weights of such a unit that minimize the relative quadratic training error within 1 or its average (over a training set) within 13/(31n) of its infimum proves..." . "loading problem; - learning complexity; - NP-hardness; - sigmoid neuron; - back-propagation; - constructive learning"@en . . "Springer-Verlag" . . "P(GA201/00/1489), P(IAB2030007), Z(AV0Z1030915)" . . "Minimizing the Quadratic Training Error of a Sigmoid Neuron is Hard." . . .