A New Initialization Technique: Truncated Towers

Artificial Neural Networks (ANN) can perform various tasks by modifying their parameters, determined by initialization methods, using activation functions and learning algorithms. Alongside chosen training dataset, suitability and success of the chosen methods determine how well they can fulfill these tasks. With the ‘Truncated Tower Distribution’, which we developed to find a better initialization method, a partial and visible increase in success rates have been achieved in classification tasks on different data sets, using neuron number-independent and neuron-number-dependent initialization methods.