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A novel method of calculating line loss in transformer district is presented and realized by programming, which is Back Propagation (BP) network model based on Levenberg-Marquardt (LM) algorithm. Establish the characteristic index system according to electric characteristics parameters of samples. The classification of samples by K-Means clustering algorithm solves the numerical dispersion of line...
Proposed a method of fault diagnosis for cold storage system, the method based on probabilistic rough set and support vector machine(SVM). Simplify the uncertain information by using probabilistic rough set of Bayes decision making. Design a multi-level classifier of SVM to fault diagnosis. Research the typical fault set of cold storage system with the proposed method. The results show the accuracy...
Classification is an important research topic in the field of image data mining. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough sets, etc. This paper proposed a method to classify the image with normal cloud model which is an uncertainty transformation model between quantities and qualities conception. We develop...
Relevance feedback is a good method for semantic gap in image retrieval. In this paper we propose a method which uses support vector machines for conducting effective relevance feedback for trademark retrieval. The algorithm takes the test results to adjust the already trained support vector machines. We select the Tamura textures features which consistent with human vision perception and the low-level...
More and more efforts have been made for the research of emotional speech recently. Although we may, sometimes be able to make a definite perceptual decision on emotion state, emotion is actually a kind of cline in a large vector space. Different emotions can be thought of as zones along an emotional vector. To resolve the ambiguity of emotion perception, the authors make an array of perception experiments...
This paper investigates an effective algorithm for inverse problem of support vector machines. The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains maximum. However the training time for inverse problem of SVM is incredible. Clustering is a feasible way to simplify the process of it, but it is difficult to estimate the number of...
Support vector machines (SVM) are favored for relevance feedback in content-based image retrieval by utilizing both positive and negative feedbacks. This paper uses incremental reduced support vector machines to get the support vectors and the non-support vectors, then utilizes both positive and negative feedbacks for image retrieval based on SVM. It needn't use the results of retrieval to train SVM...
The support vector machines have been promising methods for classification because of their solid mathematical foundation. However they are not favored for large-scale because the training complexity of SVM is highly dependent on the size of data set.This paper uses Incremental Reduced Support Vector Machines (IRSVM) which begins with an extremely small reduced set and incrementally expands the reduced...
For classification problem clustering method divides the dataset into many clusters based on correlation attribute of all elements of the dataset. How to classify data within the same clustering number as close as possible and data in different clusters as depart as possible is the key of clustering method. Clustering support vector machines (ClusterSVM) partition the training data into disjoint clusters...
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