About: A noniterative, incremental and hyperparameterfree learning method for onelayer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition. Changes:Initial Announcement on mloss.org.

About: A noniterative learning method for onelayer (no hidden layer) neural networks, where the weights can be calculated in a closedform manner, thereby avoiding low convergence rate and also hyperparameter tuning. The proposed learning method, LANNSVD in short, presents a good computational efficiency for largescale data analytic. Changes:Initial Announcement on mloss.org.
