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Wireless capsule endoscopy video summarization (WCE-VS) is highly demanded for eliminating redundant frames with high similarity. Conventional WCE-VS methods extract various hand-crafted features as image representations. Researches show that such features only reflect the low-level characteristics of single frame and essentially are not effective to capture the semantic similarity between WCE frames...
In the modern society, energy consumption such as gas and electricity is closely related to the weather condition because of the large share of weather-sensitive electrical appliances. Investigating how weather influences the energy consumption is of great significance for energy demand forecasting. This paper proposes an optimum regression approach for analyzing weather influence on the energy consumption...
Blog is becoming an increasingly popular media for information publishing. Besides the main content, most of blog pages nowadays also contain noisy information such as advertisements etc. Removing these unrelated elements can improves user experience, but also can better adapt the content to various devices such as mobile phones. Though template-based extractors are highly accurate, they may incur...
Protein complex (complex for short), is a set of proteins that interact with each other for specific biological activities. The core idea of traditional unsupervised clustering methods is finding dense subgraphs from the protein-protein interaction (PPI) network. In fact, some complexes are not dense in the network. Supervised clustering methods regard known complexes as positive cases and unknown...
Activity recognition in smart environments is an important technology for assisted living and e-health. Recently there are growing interests in applying machine learning algorithms to activity recognition tasks. In this paper, we combine support vector machine (SVM) and association rule learning to improve the performance of activity recognition based on streaming sensor data in smart homes. The proposed...
In order to early warn the alga bloom in a reservoir (drinking water source) in northeast China, we carried out a comparative investigation on three statistical machine learning methods to construct suitable one-step weekly Chlorophyll-a (Chla) prediction models: multiple linear regression (MLR), back propagation artificial neural network (BPANN) and support vector regression machine (SVR). Previously,...
This paper proposes a method of face recognition using the support vector machine (SVM) based on the fuzzy rough set theory (FRST). Firstly, features from human face images are extracted by combining the 2-D wavelet decomposition technique with the grayscale integral projection technique. And then, the attribute reduction algorithm based on FRST is applied in face recognition. The reduction algorithm...
As a developing endeavor of data mining on semi-structured information, sentiment analysis to the comments on the Internet has aroused people's great interest recently. This paper analysis the influence of different stop word removal methods on the result of text classification and represent the more effective stop word removal list. The experiment bases on the sentiment comments which have been grasped...
This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results. At...
The explosive Web make it hard to organize and manage Web information automatically. Therefore, online learning method such as incremental learning is gradually become effective instrument in practical applications. From our experiments, traditional incremental learning shows some flaws in the iterative process. To overcome the drawback caused by using only support vector to represent the whole former...
In the analysis of predicting financial distress based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone...
The particles in urinary microscopic images are hard to classify because of noisy background and strong variability of objects in shape and texture. In order to overcome these difficulties, firstly, a new method of texture feature extraction using the distance mapping based on a set of local grayvalue invariants is introduced and the feature is robust to the shift and rotation. Secondly, we reduce...
In this paper, we introduce a face recognition approach based on the contourlet transform and support vector machine, which takes technological advantages of both support vector machine and the contourlet transform for feature extraction. The contributions of this paper include the following aspects: (1) support vector machine is successfully applied to face recognition by using the contourlet transform...
A new and universal steganalysis scheme for JPEG images was proposed. Moments in frequency domain of wavelet sub-bandpsilas histogram was extracted to analyze the difference of histogram between cover image and stego image, at the same time, used high-pass filter to strengthen the image in order to make the discontinuity among blocks become more obvious, so the extracted block characteristic will...
Price forecaster is one of the important aspects in the researches of the electricity price. But because of the special properties of electricity, the price of electricity is far more volatile than that of other relatively volatile commodities. Due to such significant volatility, it is difficult to make an accurate forecast for the spot market of electricity. To solve above problem, a hybrid algorithm...
In the electricity market, load elasticity analysis has been regarded as an effective tool for quantitative analysis of the load risk analysis, which may lead the researchers a profound understanding on load volatility and help market participants make decisions. But due to the complicated load volatility under the competitive surroundings, few studies have been devoted to this. So in order to solve...
Due to the short history of electricity market, price- load elasticity is along one of focused and unsolved problems in the researches of electricity market. This paper describes a novel model for price-load elasticity analysis, where support vector machine (abbr. SVM) is applied for pattern identification of electricity price. First the correlation analysis of loads and prices are performed to choose...
In the electricity market, electricity consumption reflects the electric power usage of the whole society. As an important part of load forecasting, annual electricity consumption forecasting plays an important role for all the market participants, especially for the decision-makers establishing bidding strategies of generation companies and the planners of market investors. In this paper, factor...
Electricity consumption reflects the electricity usage of the whole society, so the prediction study and analysis to electricity consumption have important realistic and theoretical significance. The influence that different factors affect the electricity consumption is in different degrees. And the influence works in complicated ways, which makes the characteristics of the electricity consumption...
This paper describes the application of SVM to breast cancer diagnosis, which has shown good generalization. We take use of non-symmetrical C-SVM to solve the problem of unbalanced training examples. In order to gain a fast searching method for parameters of the model, a margin-based bound on generalization is more effective than traditional k-fold cross-validation. After feature subset selection...
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