Name
Abstract | ||
|---|---|---|
In this paper, we extend the exponentially embedded family (EEF), a new approach to model order estimation and probability density function construction originally proposed by Kay in 2005, to multivariate pattern recognition. Specifically, a parametric classifier rule based on the EEF is developed, in which we construct a distribution for each class based on a reference distribution. The proposed method can address different types of classification problems in either a data-driven manner or a model-driven manner. In this paper, we demonstrate its effectiveness with examples of synthetic data classification and real-life data classification in a data-driven manner and the example of power quality disturbance classification in a model-driven manner. To evaluate the classification performance of our approach, the Monte-Carlo method is used in our experiments. The promising experimental results indicate many potential applications of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TNNLS.2014.2383692 | IEEE Trans. Neural Netw. Learning Syst. |
Keywords | Field | DocType |
eef,power quality disturbance classification,monte-carlo method,probability density function,exponentially embedded family,monte carlo methods,pattern classification,parametric classification rule,estimation theory,exponentially embedded family (eef),parametric classification rule.,order estimation,reference distribution,multivariate gaussian classification,exponential distribution,data models,neural networks,training data,estimation,testing,statistics,vectors | Data modeling,Rule-based system,Classification rule,Pattern recognition,Computer science,Parametric statistics,Synthetic data,Artificial intelligence,Data classification,Artificial neural network,Classifier (linguistics),Machine learning | Journal |
Volume | Issue | ISSN |
26 | 2 | 2162-2388 |
Citations | PageRank | References |
11 | 0.71 | 9 |
Authors | ||
4 | ||