The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy engineering
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A unified approach for determining the underlying causes of non-stationary disturbances
International Journal of Computer Applications in Technology
Expert Systems with Applications: An International Journal
Discrete harmony search based expert model for epileptic seizure detection in electroencephalography
Expert Systems with Applications: An International Journal
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This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines (a novel method) as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge; however, its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the eectiveness of a classifier, data segmentation, and feature extraction are discussed. Segmentation of a sequence of data recording is preprocessing to partition the data into segments each representing a duration containing either an event or a transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating the effectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions.