Add to ORKGThis thesis investigates the generalization problem in artificial neural networks, attacking it from two major approaches: regularization and model selection
Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, Au...
Neural Networks (NN) can be trained to perform tasks such as image and handwriting recognition, cred...
To appear in the Handoobk of Brain Theory and Neural Networks, Michael Arbib, Ed. Prepared within th...
This thesis presents a new theory of generalization in neural network types of learning machines. Th...
Abstract. In this paper we address the important problem of optimizing regularization parameters in ...
AbstractA novel adaptive regularization parameter selection (ARPS) method is proposed in this paper ...
Jedan od glavnih izazova treniranja dubokih neuronskih mreža s milijunima parametara je izbjegavanje...
Learning with generalization can be modeled using regularization, which was developed for a search o...
It has been shown that the selection of the most similar training patterns to generalize a new sampl...
This final thesis covers the basics of artificial neural networks, with focus on supervised learning...
To expand the size of a real dataset, data augmentation techniques artificially create various versi...
Nowadays, in the era of complex data, the knowledge discovery process became one of the key challeng...
It has been shown that the selection of the most similar training patterns to generalize a new sampl...
In this work, we study how the selection of examples affects the learn-ing procedure in a boolean ne...
This research demonstrates a method of discriminating the numerical relationships of neural network ...
Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, Au...
Neural Networks (NN) can be trained to perform tasks such as image and handwriting recognition, cred...
To appear in the Handoobk of Brain Theory and Neural Networks, Michael Arbib, Ed. Prepared within th...
This thesis presents a new theory of generalization in neural network types of learning machines. Th...
Abstract. In this paper we address the important problem of optimizing regularization parameters in ...
AbstractA novel adaptive regularization parameter selection (ARPS) method is proposed in this paper ...
Jedan od glavnih izazova treniranja dubokih neuronskih mreža s milijunima parametara je izbjegavanje...
Learning with generalization can be modeled using regularization, which was developed for a search o...
It has been shown that the selection of the most similar training patterns to generalize a new sampl...
This final thesis covers the basics of artificial neural networks, with focus on supervised learning...
To expand the size of a real dataset, data augmentation techniques artificially create various versi...
Nowadays, in the era of complex data, the knowledge discovery process became one of the key challeng...
It has been shown that the selection of the most similar training patterns to generalize a new sampl...
In this work, we study how the selection of examples affects the learn-ing procedure in a boolean ne...
This research demonstrates a method of discriminating the numerical relationships of neural network ...
Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, Au...
Neural Networks (NN) can be trained to perform tasks such as image and handwriting recognition, cred...
To appear in the Handoobk of Brain Theory and Neural Networks, Michael Arbib, Ed. Prepared within th...