A new set of random forward weights and biases and feedback weights were chosen for each of the n=20 simulations. The elements of B1 and B2 were also drawn from a uniform distribution and fixed across simulations. Learning was terminated after the same number of iterations for each simulation...
Interval neural networks in this paper are characterized by interval weights and interval biases. This means that the weights and biases are given by intervals instead of real numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Interval ...
These Hopfield networks are governed by a single scalar parameter that controls their weights and biases. In one extreme value of this parameter, we show that the information capacity is optimal whereas the fault-tolerance is zero. At the other extreme, our results are inexact. We are only ...
The Impact of Visiting Team Travel on Game Outcome and Biases in NFL Betting Markets Using data on regular season National Football League games from 1981-2004, this study examines the impact that travel has on game outcome and whether bett... MW Nichols - 《Journal of Sports Economics》 被...
Is a vanilla-trained model already embodying some "unbiased sub-networks" that can be used in isolation and propose a solution without relying on the algorithmic biases? In this work, we show that such a sub-network typically exists, and can be extracted from a vanilla-trained model without...
Specifically, we employ balanced resampling locally at each client to rectify biases and perform cluster clients based on feature similarity, assigning weights appropriately. This strategy not only strengthens the model's capacity to learn cross-client generalizable features but also minimizes the ...
Due to the biases of training data, and local optimal of weak classifiers, some weak classifiers may not be well trained. Usually, some component classifiers of a weak classifier may be not act well as the others. This will make the performances of the weak classifier not as good as it ...
The MLP network can be trained using backpropagation, which is an iterative algorithm that adjusts the weights and biases of the network to minimize aoss function that measures the difference between the predicted output and the true output. The weights and biases are updated using the gradient ...
The MLP network can be trained using backpropagation, which is an iterative algorithm that adjusts the weights and biases of the network to minimize aoss function that measures the difference between the predicted output and the true output. The weights and biases are updated using the gradient ...
The MLP network can be trained using backpropagation, which is an iterative algorithm that adjusts the weights and biases of the network to minimize aoss function that measures the difference between the predicted output and the true output. The weights and biases are updated using the gradient ...