The OJA neural network, also known as Oja's rule neural network, is a type of artificial neural network used for unsupervised learning in dimension reduction and principal component analysis problems.
It was developed by Finnish mathematician Erkki Oja in 1982 and is based on a learning algorithm that allows the neural network to find the principal directions of the input features and reduce the dimension of the data. Compared to other dimension reduction methods, the OJA neural network is better able to handle highly correlated and non-linear data.
The operation of the OJA neural network is based on adjusting the synaptic weights of the network so that the output neuron responds selectively to specific input patterns. This is achieved by iteratively calculating the synaptic weights to maximise the correlation between the inputs and the output neuron.
Hoy, 3 de octubre, hemos estado en los prestigiosos "Premios SCALEUPS B2B organizada por la Fundación Empresa y Sociedad, para hablaros de la Medici [...]
Read More »Fernando Pavón, CEO of Gamco and expert in Artificial Intelligence applied to business explains to us in the AceleraPYMES cycle how small companies can [...]
Read More »In the dynamic financial world, optimizing the return on available assets is essential to the success of any lender. Gam [...]
Read More »Collecting debts, nowadays, is becoming an arduous task for many companies or freelancers. More and more banks, debt collection [...]
Read More »
