We investigate the learning rate of multiple kernel leaning (MKL) with l1 and elastic-net regularizations. The elastic-net regularization is a composition of an l1-regularizer for inducing the sparsity and an l2-regularizer for controlling the smoothness. We focus on a sparse setting where the total number of kernels is large but the number of non-zero components of the ground truth is relative...

In this paper, we present a novel neural network architecture called M-net, which exploits the don't-care information in training multilayer feedforward neural networks. Our method takes advantage of the user's prior knowledge as well as the neural network's ability to learn from examples. The user's prior knowledge is encoded in the form of don't-care inputs to reduce the number of training pa...

Incremental Net Pro IncNet Pro with local learning feature and statistically controlled growing and pruning of the network is intro duced The architecture of the net is based on RBF networks Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm IncNet Pro is similar to the Resource Allocation Network described by Platt in the main idea of the expan...

The rapid rise in popularity of multimedia applications, such as VoIP, IPTV and Video Conferencing, intensifies the need to consider resource management for user satisfaction. Furthermore, improving Quality of Experience (QoE) in Software Defined Networks (SDNs) services is one of the important issues to be addressed by provisioning optimum resource management. In this paper, resource allocatio...

Net-zero has proved a rapid and powerful convening concept for climate policy. Rather than treating it as novel development from the perspective of policy, we examine net-zero in context longer history experience “no-net-loss” framing biodiversity Drawing on material scholarly, policy activist literature cultural political economy theory, interpret turn to “net” policies practices part neoliber...

Adaptive learning is a new approach for e-learning systems. In comparison to traditional e-learning systems, which present same things for all learners, these systems automatically adapt with learner characteristics. In this paper, we are going to propose a new method for Adaptive learning, and consider adaptation from three viewpoints: 1) learner learning style; 2) learner’s knowledge level; 3...

What are neural nets? The biological model: The human brain The components of a neural net What they can and where they fail The learning process What means "learning" refering to neural nets? Supervised and unsupervised learning Forwardpropagation Backpropagation Selforganization

In this paper we consider the modeling of a selected portion of signal transduction events involved in the angiogenesis process. The detailed model of this process contains a large number of parameters and the data available from wet-lab experiments are not sufficient to obtain reliable estimates for all of them. To overcome this problem, we suggest ways to simplify the detailed representation ...