18316871
Fast Kernel Classifier Construction Using Orthogonal Forward Selection to Minimise Leave-One-Out Misclassification Rate
We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier’s generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.
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.
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.
10796129
A Forward Constrained Selection Algorithm for Probabilistic Neural Network
A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples and the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.
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.
2411991
Robust nonlinear model identification methods using forward regression
In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.
1889768
LANMAR: landmark routing for large scale wireless ad hoc networks with group mobility
In this paper, we present a novel routing protocol for wireless ad hoc networks - Landmark Ad Hoc Routing (LANMAR). LANMAR combines the features of Fisheye State Routing (FSR) and Landmark routing. The key novelty is the use of landmarks for each set of nodes which move as a group (e.g., a team of co-workers at a convention or a tank battalion in the battlefield) in order to reduce routing update overhead. Like in FSR, nodes exchange link state only with their neighbors. Routes within Fisheye scope are accurate, while routes to remote groups of nodes are "summarized" by the corresponding landmarks. A packet directed to a remote destination initially aims at the Landmark; as it gets closer to destination it eventually switches to the accurate route provided by Fisheye. Simulation experiments show that LANMAR provides efficient and scalable routing in large, mobile, ad hoc environments in which group mobility applies.
2137876
Achieving Anonymity in Mobile Ad Hoc Networks Using Fuzzy Position Information
Traditionally the anonymity of an entity of interest can be achieved by hiding it among a group of other entities with similar characteristics, i.e., an anonymity set. In mobile ad hoc networks, generating and maintaining such an anonymity set for any ad hoc node are challenging because of the node mobility and consequently of the dynamic network topology. In this paper, we address the problem of the destination anonymity. We propose protocols that use fuzzy destination position to generate a geographic area called anonymity zone (AZ). A packet for a destination is delivered to all the nodes in the AZ, which, consequently, make up the anonymity set. The size of the anonymity set may decrease because nodes are mobile, yet the corresponding management on anonymity set is simple. We design techniques to further improve node anonymity. We use extensive simulation to study the node anonymity and routing performance, and to determine the parameters that most impact the anonymity level that can be achieved by our protocol.