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The generation of weights is an alternative method of loading a set of weights into an artificial neural network. It is a process that transforms a trained base net by multiplying its weights by symmetric matrices [1]. These weights are then assigned to a derived net. The derived nets map symmetrically related functions. At present, the process is limited because it cannot be applied to one-to-many...
Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. Since E-Learning systems are digital by their very nature, their introduction rises the issue of modeling the didactic design in a way that implies the chance to apply Knowledge Engineering Techniques (like Machine Learning and Data Mining). A modeling approach called storyboarding, is outlined here. Storyboarding...
The RoboCup Simulation League is recognized as a test bed for research on multi-agent learning. As an example of multi-agent learning in a soccer game, we dealt with a learning problem between a kicker and a receiver when a direct free kick is awarded just outside the opponentpsilas penalty area. In such a situation, to which point should the kicker kick the ball? We propose a function that expresses...
Representation of knowledge within a neural model is an active field of research involved with the development of alternative structures, training algorithms, learning modes and applications. Radial Basis Function Neural Networks (RBFNNs) constitute an important part of the neural networks research as the operating principle is to discover and exploit similarities between an input vector and a feature...
This study focuses on the design of a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN) control for the path tracking of a nonholonomic mobile robot. In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic...
Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform...
Spam messages pose a major threat to the usability of electronic mail. Spam wastes time and money for network users and administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium to distribute harmful code and/or offensive content. In this paper, we investigate the application of abductive learning in filtering out spam messages. We...
Semi-supervised learning has received much attention recently. Co-training is a kind of semi-supervised learning method which uses unlabeled data to improve the performance of standard supervised learning algorithms. A novel co-training style algorithm, RASCO (for RAndom Subspace CO-training), is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation. The...
One-time password (OTP) is always used as the strongest authentication scheme among all password-based solutions. Currently, consumer devices such as smart card have implemented OTP based two-factor authentications for secure access controls. Such solutions are economically sound without support of timestamp mechanisms. Therefore, synchronization of internal parameters in OTP models, such as moving...
ISOMAP is a manifold learning based algorithm for dimensionality reduction, which is successfully applied to data visualization. However, there exists such limitation in classical ISOMAP that the algorithm is sensitive to noises, especially outliers. So in this paper an extended ISOMAP algorithm is put forward to solve the problem of sensitivity. The proposed algorithm follows the method of classical...
In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as...
Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this context, i.e. each meta-example, stores the features of a given problem and information about the empirical performance obtained by the candidate algorithms on that problem. The process of constructing a set of meta-examples may be expensive,...
Computational intelligence methods are well-suited for use in computer controlled opponents for video games. In many other applications of these methods, the aim is to simulate near-optimal intelligent behaviour. But in video games, the aim is to provide interesting opponents for human players, not optimal ones. In this study, we trained neural network-based computer controlled opponents to play like...
This article presents the hardware implementation of the radial basis function (RBF) neural network whose internal weights are updated in the real-time fashion by the back propagation algorithm. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel processing calculation of the back-propagation algorithm. The...
The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the...
We consider the multi-valued discrete real training set that can not be separated by one multi-valued multi-threshold neuron. Such training set is defined as linearly nonseparable set in this paper. Our objective is to use multi-valued multi-threshold neural networks to learn nonseparable training sets. First we give the method that how to determine a training set is separable or nonseparable (i.e...
Gene expression based cancer classification using classifier ensembles is the main focus of this work. A new ensemble method is proposed that combines predictions of a small number of k-nearest neighbor (k-NN) classifiers with majority vote. Diversity of predictions is guaranteed by assigning a separate feature subset, randomly sampled from the original set of features, to each classifier. Accuracy...
The incremental learning system for a feature extraction unit in the character recognition system is described and experimental results are shown. The relationship between this learning system and neural networks (NN) are explained and the specifications of this method are described as an NN application. The improved version of this system which is related to the Gabor filter was tested and an accuracy...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses...
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