Titlebook: Artificial Neural Networks - ICANN 96; 6th International Co Christoph Malsburg,Werner Seelen,Bernhard Sendhoff Conference proceedings 1996

Gullet

書目名稱Artificial Neural Networks - ICANN 96影響因子(影響力)




書目名稱Artificial Neural Networks - ICANN 96影響因子(影響力)學(xué)科排名




書目名稱Artificial Neural Networks - ICANN 96網(wǎng)絡(luò)公開度




書目名稱Artificial Neural Networks - ICANN 96網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Neural Networks - ICANN 96被引頻次




書目名稱Artificial Neural Networks - ICANN 96被引頻次學(xué)科排名




書目名稱Artificial Neural Networks - ICANN 96年度引用




書目名稱Artificial Neural Networks - ICANN 96年度引用學(xué)科排名




書目名稱Artificial Neural Networks - ICANN 96讀者反饋




書目名稱Artificial Neural Networks - ICANN 96讀者反饋學(xué)科排名

ROOF

Creditee

Autoassociative memory with high storage capacity,y with the number of inputs per neuron is far greater than the linear growth in the famous Hopfield network [2]. This paper shows that the GNU attains an even higher capacity with the use of pyramids of neurons instead of single neurons as its nodes. The paper also shows that the storage capacity/co

小畫像

易改變

Concerto

繁榮地區(qū)

Bayesian inference of noise levels in regression,puts, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input v

持續(xù)

Complexity reduction in probabilistic neural networks,tionally prohibitive, as all training data need to be stored and each individual training vector gives rise to a new term of the estimate. Given an original training sample of size . in a .-dimensional space, a simple binned kernel estimate with .+4) terms can be shown to attain an estimation accura

mettlesome

exquisite

Regularization by early stopping in single layer perceptron training,scriminant function. On the way between these two classifiers one has a regularized discriminant analysis. That is equivalent to the “weight decay” regularization term added to the cost function. Thus early stopping plays a role of regularization of the network.