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Statistical learning theory

About: Statistical learning theory is a research topic. Over the lifetime, 1618 publications have been published within this topic receiving 158033 citations.


Papers
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Journal ArticleDOI
TL;DR: A general approach to quantifying the size of generalization errors for margin-based classification for convex and non-convex margin classifiers, among which includes, support vector machines, kernel logistic regression, and ψ -learning is developed.

9 citations

Proceedings ArticleDOI
15 May 2009
TL;DR: The method for power transformer fault diagnosis based on SVM can be applied to the diagnosis of gear faults and the testing results show that this method can successfully be applied in this paper.
Abstract: Support Vector Machines (SVM) is a machine-learning algorithm based on statistical learning theory. The method for power transformer fault diagnosis based on SVM is proposed in this paper. The principle and algorithm of this method are introduced. Through a finite learning sample the relation is established between the transformer fault signature and the quantity of its dissolved gas. A faults classifier is constructed by using the dissolved gas data of the fault transformer. The testing results show that this method can successfully be applied to the diagnosis of gear faults.

9 citations

Dissertation
01 May 2007
TL;DR: It was concluded that it is possible to develop a classification system based on the Support Vector Machine method with “global settings” that can be used at any location without the need to retrain and shows sufficiently high classification accuracy when trained on data that originate from real disturbances.
Abstract: Developments in measurement technology, communication and data storage have resulted in measurement systems that produce large amount of data. Together with the long existing need for characterizing the performance of the power system this has ressaulted in demand for automatic and efficient information-extraction methods. The objective of the research work presented in this thesis was therefore to develop new robust methods that extract additional information from voltage and current measurements in power systems. This work has contributed to two specific areas of interest. The first part of the work has been the development of a measurement method that gives information how voltage flicker propagates (with respect to a monitoring point) and how to trace a flicker source. As part of this work the quantity of flicker power has been defined and integrated in a perceptionally relevant measurement method. The method has been validated by theoretical analysis, by simulations, and by two field tests (at low-voltage and at 130-kV level) with results that matched the theory. The conclusion of this part of the work is that flicker power can be used for efficient tracing of a flicker source and to determine how flicker propagates. The second part of the work has been the development of a voltage disturbance classification system based on the statistical learning theory-based Support Vector Machine method. The classification system shows always high classification accuracy when training data and test data originate from the same source. High classification accuracy is also obtained when training data originate from one power network and test data from another. The classification system shows, however, lower performance when training data is synthetic and test data originate from real power networks. It was concluded that it is possible to develop a classification system based on the Support Vector Machine method with “global settings” that can be used at any location without the need to retrain. The conclusion is that the proposed classification system works well and shows sufficiently high classification accuracy when trained on data that originate from real disturbances. However, more research activities are needed in order to generate synthetic data that have statistical characteristics close enough to real disturbances to replace actual recordings as training data.

9 citations

Journal Article
TL;DR: The latest developments in SVM training algorithms in domestic and overseas research were reviewed, especially reduction algorithms and algorithms with linear convergence properties.
Abstract: Support vector machines(SVMs) use new methods that originated in statistical learning theoryTraining of an SVM can be formulated as a quadratic programming problemThe principles of SVM have been summarized briefly in this paperThe latest developments in SVM training algorithms in domestic and overseas research were reviewed,especially reduction algorithms and algorithms with linear convergence propertiesThe performance of these algorithms was then compared,and a brief introduction to a proposed extension of them was givenFinally some problems and potential directions for future research are discussed

9 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202219
202159
202069
201972
201847