15393741
Backward Elimination Methods for Associative Memory Network Pruning
Three hybrid data based model construction/pruning formula are introduced by using backward elimination as automatic postprocessing approaches to improved model sparsity. Each of these approaches is based on a composite cost function between the model fit and one of three terms of A-/D-optimality / (parameter 1-norm in basis pursuit) that determines a pruning process. The A-/D-optimality based pruning formula contain some orthogonalisation between the pruned model and the deleted regressor. The basis pursuit cost function is derived as a simple formula without need for an orthogonalisation process. These different approaches to parsimonious data based modelling are applied to the same numerical examples in parallel to demonstrate their computational effectiveness.
6169265
Automatic Kernel Regression Modelling Using Combined Leave-One-Out Test Score and Regularised Orthogonal Least Squares
This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least squares. The proposed algorithm aims to achieve maximised model robustness via two effective and complementary approaches, parameter regularisation via ridge regression and model optimal generalisation structure selection. The major contributions are to derive the PRESS error in a regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised orthogonal least squares forward regression framework and hence construct a model with a good generalisation property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated model construction procedure without resort to any other validation data set for model evaluation.
20923699
On improvement of classification accuracy for stochastic discrimination
In this paper, an improved stochastic discrimination (SD) is introduced to reduce the error rate of the standard SD in the context of multi-class classification problem. The learning procedure of the improved SD consists of two stages. In the first stage, a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. In the second stage, the standard SD is modified by (i) restricting sampling in the important space; and (ii) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but smaller variance than that of standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples axe provided to demonstrate the effectiveness of the proposed improved SD.
12169365
Orthogonal Forward Selection for Constructing the Radial Basis Function Network with Tunable Nodes
An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.
9825053
M-estimator and D-optimality model construction using orthogonal forward regression
This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.
30515858
Protecting Location Privacy with Dynamic Mac Address Exchanging in Wireless Networks
Location information of users can now be collected from most wireless communication using advanced wireless location tracking techniques. Providing location information can be advantageous in some situations. However, there are instances, where it may be critical to protect the location of the individual. Several protection strategies, such as periodically updating interface identifiers, have been proposed so that an adversary cannot track mobiles in long-term movements. In this paper, we introduce a new strategy, DMAS (Dynamical Mac Assignment with Shuffle), in which the mobile client dynamically exchanges its assigned Mac addresses with others.We present a security analysis to show this scheme can greatly secure a client's location privacy.
2606413
A Mobility Framework for Ad Hoc Wireless Networks
Mobility management in ad hoc wireless networks faces many challenges. Mobility constantly causes the network topology to change. In order to keep accurate routes, the routing protocols must dynamically readjust to such changes. Thus, routing update traffic overhead is significantly high. Different mobility patterns have in general different impact on a specific network protocol or application. Consequently the network performance will be strongly influenced by the nature of the mobility pattern. In the past, mobility models were rather casually used to evaluate network performance under different routing protocols. Here, we propose a universal mobility framework, Mobility Vector Model, which can be used for recreating the various mobility patterns produced in different applications. Case studies on optimal transmission range as a function of mobility and on network performance under various mobility models are presented in the paper. Simulation results show that excessively large transmission range will not improve network performance significantly because of the increased collisions. There is an optimal range between 1.5 - 2 times the mean node distance for free space channel. Also, simulation results show that different mobility models will have different impact on the network performance for a variety of routing protocols (AODV, DSR, FSR). When choosing routing protocols for ad hoc network applications, performance studies under multiple mobility models are recommended. The Mobility Vector model can provide a realistic and flexible framework for reproducing various models.