Unconstrained growth of synaptic activity and lack of references to synaptic depression in Hebb‟s postulate have diminished its value as a learning algorithm. The existing synaptic scaling mechanisms such as weight normalization, threshold updating, and spike time dependent plasticity have greatly contributed to address these issues in Hebb‟s postulate. However, all these mechanisms are based o...

The weight with which a specific outcome feature contributes to preference quantifies a person's 'taste' for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncer...

In this paper we propose an algorithm to classify tensor data. Our methodology is built on recent studies about matrix classification with the trace norm constrained weight matrix and the tensor trace norm. Similar to matrix classification, the tensor classification is formulated as a convex optimization problem which can be solved by using the off-the-shelf accelerated proximal gradient (APG) ...

This paper develops a technique for dominant pole localization of the adaptation process, performed by the FxLMS algorithm in active noise control systems. This development results in a new adaptation algorithm, called Filtered Weight FxLMS (FwFxLMS). Similar to the FxLMS, FwFxLMS uses a recursion for updating the weight vector of a transversal ANC controller. However, in the new algorithm, the...

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In ...

In the last few lectures, we talked about various kinds of online learning problems. We started with the simplest case in which there is a perfect expert, who is always right. In the last lecture, we generalize this into the case in which we might not have a “perfect expert”, but instead we have a panel or subcommittee of experts, whose combined prediction is always right. We also introduced th...

In this paper, a control strategy that integrates a kinematic controller and an adaptive wavelet neural network (AWNN) controller for nonholonomic mobile robots including actuator dynamics is presented. The AWNN control system adopts a wavelet neural network (WNN) with accurate approximation capability to represent the unknown dynamics of the robotic system. It also uses a robust term to confro...

Finding central nodes is a fundamental problem in network analysis. Betweenness centrality is a well-known measure which quantifies the importance of a node based on the fraction of shortest paths going though it. Due to the dynamic nature of many today’s networks, algorithms that quickly update centrality scores have become a necessity. For betweenness, several dynamic algorithms have been pro...

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to c...

In this paper, we propose a new model that allows us to investigate this competitive aspect of real networks in quantitative terms. Through theoretical analysis and numerical simulations, we find that the competitive network have the universality for a weighted network. The relation between parameters in the weighted network and the competitiveness in the competitive network is obtained. So we ...