This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact network structure dynamics and product diffusion. In directed networks, all direct indirect links contribute agents' learning. comparison, welfare are lower in undirected with cliques. rich class behavior is described by small number differe...

In contradiction with Hopfield-like networks, random recurrent neural networks (RRNN), where the couplings are random, exhibit complex dynamics (limit cycles, chaos). It is possible to store information in these networks through hebbian learning. Eventually, learning “destroys” the dynamics and leads to a fixed point attractor. We investigate here the structural change in the networks through l...

The paper is focused on neural networks, their learning algorithms, special architecture and SVM. General learning rule as a function of the incoming signals is discussed. Other learning rules such as Hebbian learning, delta learning, perceptron learning, Least Mean Square (LMS) learning, Winner Take All (WTA) learning are presented as a derivation of the general learning rule. Architecture spe...

A functional equivalence of feed-forward networks has been proposed to reduce the search space of learning algorithms. A novel genetic learning algorithm for RBF networks and perceptrons with one hidden layer that makes use of this theoretical property is proposed. Experimental results show that our procedure outperforms the standard genetic learning. Key-Words: Feedforward neural networks, gen...

Routing in computer networks has played a special role in recent years. The cause of this is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider p...

|This literature review discusses di erent methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the di erentmethodological communities, such as Bayesian, description length, and classical statistic...

While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argument...

Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In...