1530302
A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps
Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focuses on finding a neuron that is most similar to that of an input vector. Since an update of a neuron only benefits part of the feature map, it can be thought of as a local optimization problem. The ability to move away from a local optimization model into a global optimization model requires the use of game theory techniques to analyze overall quality of the SOM. A new algorithm GTSOM is introduced to take into account cluster quality measurements and dynamically modify learning rates to ensure improved quality through successive iterations.
12580116
Supporting Research with Weblogs: A Study on Web-Based Research Support Systems
Research environments have been changing with the availability of new technologies for decades. Researchers are benefited from digital libraries, online databases, and Web search engines recently. It is expected that the Web-based research systems play an important role in supporting research activities. Blogs, a new form of mainstream personal communication and a new voice for traditional media, recently gained much attention from the public, industrial and academic. This article studies issues on Web-based research support systems with blogs
14229561
Time dependent directional profit model for financial time series forecasting
Goodness-of-fit is the most popular criterion for neural network time series forecasting. In the context of financial time series forecasting, we are not only concerned at how good the forecasts fit their targets, but we are more interested in profits. In order to increase the forecastability in terms of profit earning, we propose a profit based adjusted weight factor for backpropagation network training. Instead of using the traditional least squares error, we add a factor which contains the profit, direction, and time information to the error function. The results show that this new approach does improve the forecastability of neural network models, for the financial application domain.
12851279
Ecommerce Adoption of Insurance Companies in New Zealand
Electronic commerce is causing fundamental changes in the insurance industry. The New Zealand insurance industry is well positioned to benefit from electronic commerce due to the high level of online access technology, high Internet usage, the high number of websites and the fact that New Zealanders are usually quick to embrace new technology. However, despite the emergence of e-commerce in the New Zealand information technology and insurance industry literature, little empirical research has been reported in the New Zealand insurance industry. This research presents an overview of the current state of e-commerce in the New Zealand insurance industry. In order to measure the extent of e-commerce usage, a descriptive study that surveys website content was designed. The survey found that all of the insurance companies had websites but less than half of these were New Zealand ones. Most of the companies use an international website to service all their global customers, yet the majority of these sites do not include many features specific for New Zealand customers. Overall it was found that the uptake of electronic commerce is relatively slow. The insurance industry is only employing the Internet to distribute information, with few companies offering the capability of online transactions. It is evident that there remains considerable scope for further development of website features.
5263048
On Web-based Support Systems
Web-based Support Systems (WSS) is viewed as a multidisciplinary research that focuses on supporting human activities in specific domains or fields based on computer science, information technology, and Web technology. Research on WSS is motivated by the challenges and opportunities of the Web and the recent advancements of computer and Web technologies which make the implementation of WSS feasible. Fundamental issues of WSS are examined, a framework of WSS is presented, and research on WSS is discussed in this article. Some concrete examples such as Web-based research support systems (WRSS) and Web-based information retrieval support systems (WIRSS) are also discussed. It is expected that researchers will pay more attention on the new research area on WSS.
18829938
An Enhanced Support Vector Machine Model for Intrusion Detection
Design and implementation of intrusion detection systems remain an important research issue in order to maintain proper network security. Support Vector Machines (SVM) as a classical pattern recognition tool have been widely used for intrusion detection. However, conventional SVM methods do not concern different characteristics of features in building an intrusion detection system. We propose an enhanced SVM model with a weighted kernel function based on features of the training data for intrusion detection. Rough set theory is adopted to perform a feature ranking and selection task of the new model. We evaluate the new model with the KDD dataset and the UNM dataset. It is suggested that the proposed model outperformed the conventional SVM in precision, computation time, and false negative rate
8612227
Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation
The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes. We adopt the mixed-state Markov random fields (MRF) models recently introduced to represent so-called motion textures. The approach consists in describing the spatial distribution of some motion measurements which exhibit mixed-state nature: a discrete component related to the absence of motion and a continuous part for measurements different from zero. We propose several significative extensions to this model. We define an original motion texture segmentation method which does not assume conditional independence of the observations for each texture and normalizing factors are properly handled. Results on real examples demonstrate the accuracy and efficiency of our method.
62113571
Learned probabilistic image motion models for event detection in videos
We present new probabilistic motion models of interest for the detection of relevant dynamic contents (or events) in videos. We separately handle the dominant image motion assumed to be due to the camera motion and the residual image motion related to scene motion. These two motion components are then represented by different probabilistic models which are further recombined for the event detection task. The motion models associated to pre-identified classes of meaningful events are learned from a training set of video samples. The event detection scheme proceeds in two steps which exploit different kinds of information and allow us to progressively select the video segments of interest using maximum likelihood (ML) criteria. The efficiency of the proposed approach is demonstrated on sports videos.
41338736
An approach to identification of variances for radar tracking systems
An approach for the identification of the variances of the process noise W(k) and the measurement noise V(k) for radar tracking systems is presented. In the proposed algorithm, the variances are expressed as a linear combination of the covariances of the third-order difference of the measurement data. The variances can then be estimated by estimating the covariances of the third-order difference and solving the matrix equation. The two cases for the measurement noise being white and color are considered in this algorithm. Simulation results show that for both cases a steady estimation is reached after 20 samples.
108001
Cournot Equilibrium in Price-Capped Two-Settlement Electricity Markets
We compare two alternative mechanisms for capping prices in two-settlement electricity markets. With sufficient lead time and competitive entry opportunities, forward market prices are implicitly capped by competitive pressure of potential entry that will occur when forward prices rise above a certain level. Another more direct approach is to cap spot prices through regulatory intervention. In this paper we explore the implications of the two alternative mechanisms in a two settlement Cournot equilibrium framework. We formulate the market equilibrium as a stochastic equilibrium problem with equilibrium constraints (EPEC) capturing congestion effects, probabilistic contingencies and market power. As an illustrative test case we use the 53-bus Belgian electricity network with representative generator cost but hypothetical demand and ownership assumptions. When compared to two-settlement systems without price caps we find that either of the price capping alternatives results in reduced forward contracting. Furthermore the reduction in spot prices due to forward contracting is smaller.
10944826
Recognition of Dynamic Video Contents With Global Probabilistic Models of Visual Motion
The exploitation of video data requires methods able to extract high-level information from the images. Video summarization, video retrieval, or video surveillance are examples of applications. In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features. We adopt a statistical approach involving modeling, (supervised) learning, and classification issues. Because of the diversity of video content (even for a given class of events), we have to design appropriate models of visual motion and learn them from videos. We have defined original parsimonious global probabilistic motion models, both for the dominant image motion (assumed to be due to the camera motion) and the residual image motion (related to scene motion). Motion measurements include affine motion models to capture the camera motion and low-level local motion features to account for scene motion. Motion learning and recognition are solved using maximum likelihood criteria. To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos