Topic
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 published on a yearly basis
Papers
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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
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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
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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
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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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9 citations