2932585
A Granular Computing Approach to Machine Learning
The key to granular computing is to make use of granules in problem solving. Classification is one of the well studied problems in machine learning and data mining as it involves of discovery knowledge from large databases. We presents a granular computing view to classification problems and propose a granular computing approach to classification in this paper. The ID3 [8] and PRISM [2] algorithma are studied and extended to granular computing algorithm.
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.
821925
Knowledge Based Descriptive Neural Networks
This paper presents a study of knowledge based descriptive neural networks (DNN). DNN is a neural network that incorporates rules extracted from trained neural networks. One of the major drawbacks of neural network models is that they could not explain what they have done. Extracting rules from trained neural networks is one of the solutions. However, how to effectively use extracted rules has been paid little attention. This paper addresses issues of effective ways of using these extracted rules. With the introduction of DNN, we not only keep the good feature of nonlinearity in neural networks but also have explanation of underlying reasoning mechanisms, for instance, how prediction is made.
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
13215822
Feature Selection with Adjustable Criteria
We present a study on a rough set based approach for feature selection. Instead of using significance or support, Parameterized Average Support Heuristic (PASH) considers the overall quality of the potential set of rules. It will produce a set of rules with balanced support distribution over all decision classes. Adjustable parameters of PASH can help users with different levels of approximation needs to extract predictive rules that may be ignored by other methods. This paper finetunes the PASH heuristic and provides experimental results to PASH.
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.
18387908
Extraction of Semantic Dynamic Content from Videos with Probabilistic Motion Models
The exploitation of video data requires to extract information at a rather semantic level, and then, methods able to infer “concepts” from low-level video features. We adopt a statistical approach and we focus on motion information. Because of the diversity of dynamic video content (even for a given type of events), we have to design appropriate motion models and learn them from videos. We have defined original and parsimonious probabilistic motion models, both for the dominant image motion (camera motion) and the residual image motion (scene motion). These models are learnt off-line. Motion measurements include affine motion models to capture the camera motion, and local motion features for scene motion. The two-step event detection scheme consists in pre-selecting the video segments of potential interest, and then in recognizing the specified events among the pre-selected segments, the recognition being stated as a classification problem. We report accurate results on several sports videos.
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.
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
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.
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.