… One main problem associated with unsupervised learning is how to find the hidden … on
the training/learning of unsupervised learning based on artificial neural network. It provides a …

… In this paper we propose a neural network training algorithm which converges to exactly
this solution. We prove that the network converges from any initially random set of weights to …

… neural network learning algorithms which are related to Principal Components Analysis (PCA).
These algorithms learn … could be learned in an unsupervised neural network framework, …

… an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained
convolutional neural network … We also learn the explainer to use object-part features to …

… of neural network has been investigated in many studies [1–14]. This paper deals with an
unsupervised learning … for the training of deep neural networks. The conventional approach to …

… first step for a typical unsupervised learning technique is the estimation of these clusters. …
We present here a new method for unsupervised learning in neural networks (see also Atiya, …

… The essence of the approach introduced in this study is to construct a neural network that …
(pattern) is transformed according to the nonlinear mapping realized by the neural network …

… in ordinary learning and conditioning. Self-organization has been a major preoccupation
of those interested in neural networks since the early days, and the volume edited by Yovits …

… of the unsupervised learning techniques … unsupervised artificial neural networks algorithms
including autoencoders and selforganizing maps. Unsupervised artificial neural networks …

… learning input data item. For the purposes of clustering-based research, we focused on
unsupervised learning … More specifically, we used Kohonen's learning rule and a variation on the …