Search results for "VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425"
showing 10 items of 25 documents
Networking logistic neurons can yield chaotic and pattern recognition properties
2011
Accepted version of an article the book: 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings. Published version available from IEEE: http://dx.doi.org/10.1109/CIMSA.2011.6059914 Over the last few years, the field of Chaotic Neural Networks (CNNs) has been extensively studied because of their potential applications in the understanding/recognition of patterns and images, their associative memory properties, their relationship to complex dynamic system control, and their capabilities in the modeling and analysis of other measurement systems. However, the results concerning CNNs which can demonstrate chaos, quasi-chaos, …
A Stochastic Search on the Line-Based Solution to Discretized Estimation
2012
Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_77 Recently, Oommen and Rueda [11] presented a strategy by which the parameters of a binomial/multinomial distribution can be estimated when the underlying distribution is nonstationary. The method has been referred to as the Stochastic Learning Weak Estimator (SLWE), and is based on the principles of continuous stochastic Learning Automata (LA). In this paper, we consider a new family of stochastic discretized weak estimators pertinent to tracking time-varying binomial distributions. As opposed to the SLWE, our p…
Finding Optimal Rush Attacks in Real Time Strategy (RTS) Games
2008
Masteroppgave i informasjons- og kommunikasjonsteknologi 2008 – Universitetet i Agder, Grimstad What will you fell when play with an unchangeable AI in RTS game? Of cause, it is boring. You can beat them easily and that’s no fun. In this research, we will try to design an AI with learning-ability and return the fun to players. We firstly abstract a simple AI mode, and then implement a suitable learning method . Due to some developing problems, we simulate the system (ORTS). Finally, we establish a new learning system for RTS AI. It’s an advanced point system based on the conception of the evaluation system in commercial RTS game . Decision making processes could depend on the points of each…
Improving the Performance Metric of Wireless Sensor Networks with Clustering Markov Chain Model and Multilevel Fusion
2013
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2013/783543 Open access The paper proposes a performance metric evaluation for a distributed detection wireless sensor network with respect to IEEE 802.15.4 standard. A distributed detection scheme is considered with presence of the fusion node and organized sensors into the clustering and non-clustering networks. Sensors are distributed in clusters uniformly and nonuniformly and network has multilevel fusion centers. Fusion centers act as heads of clusters for decision making based on majority-like received signal strength (RSS) with comparis…
On the pattern recognition and classification of stochastically episodic events
2012
Published version of a chapter published in the book: Transactions on Compuational Collective Intelligence VI. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-29356-6_1 Researchers in the field of Pattern Recognition (PR) have traditionally presumed the availability of a representative set of data drawn from the classes of interest, say ω 1 and ω 2 in a 2-class problem. These samples are typically utilized in the development of the system’s discriminant function. It is, however, widely recognized that there exists a particularly challenging class of PR problems for which a representative set is not available for the second class, which has motivated a great deal of…
On achieving near-optimal “Anti-Bayesian” Order Statistics-Based classification fora asymmetric exponential distributions
2013
Published version of a Chapter in the book: Computer Analysis of Images and Patterns. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-40261-6_44 This paper considers the use of Order Statistics (OS) in the theory of Pattern Recognition (PR). The pioneering work on using OS for classification was presented in [1] for the Uniform distribution, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean - which is distinct from the optimal Bayesian paradigm. In [2], we showed that the results could be extended for a few sym…
Tracking the Preferences of Users Using Weak Estimators
2011
Published version of am article from the book:AI 2011: Advances in Artificial Intelligence. Also available from the publisher on SpringerLink:http://dx.doi.org/10.1007/978-3-642-25832-9_81 Since a social network, by definition, is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary, estimating a user’s interests, typically, involves non-stationary distributions. The consequent time varying nature of the distribution to be trac…
On using prototype reduction schemes to optimize locally linear reconstruction methods
2012
Authors version of an article published in the journal: Pattern Recognition. Also available from the publisher at: http://dx.doi.org/10.1016/j.patcog.2011.06.021 This paper concerns the use of prototype reduction schemes (PRS) to optimize the computations involved in typical k-nearest neighbor (k-NN) rules. These rules have been successfully used for decades in statistical pattern recognition (PR) [1,15] applications and are particularly effective for density estimation, classification, and regression because of the known error bounds that they possess. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a pri…
A new frontier in novelty detection: Pattern recognition of stochastically episodic events
2011
Published version of an article from the book: Intelligent Information and Database Systems, Lecture Notes in Computer Science. Also available from the publisher on SpringerLink:http://dx.doi.org/10.1007/978-3-642-20039-7_44 A particularly challenging class of PR problems in which the, generally required, representative set of data drawn from the second class is unavailable, has recently received much consideration under the guise of One-Class (OC) classification. In this paper, we extend the frontiers of OC classification by the introduction of a new field of problems open for analysis. In particular, we note that this new realm deviates from the standard set of OC problems based on the fo…
Learning automata-based solutions to the optimal web polling problem modelled as a nonlinear fractional knapsack problem
2011
Accepted version of an article from the journal: Engineering Applications of Artificial Intelligence. Definitive published version on Elsevier Science Direct: http://dx.doi.org/10.1016/j.engappai.2011.05.018 We consider the problem of polling web pages as a strategy for monitoring the world wide web. The problem consists of repeatedly polling a selection of web pages so that changes that occur over time are detected. In particular, we consider the case where we are constrained to poll a maximum number of web pages per unit of time, and this constraint is typically dictated by the governing communication bandwidth, and by the speed limitations associated with the processing. Since only a fra…