Research and simulation of generalized statistical pattern recognition

Haibo Gao, Yingying Wang, Jianxin Cui, Yao Gao, Jinsheng Yang*, Aiping LYU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Statistical pattern recognition techniques are exercising dominion over nowadays. But there are many real-world problems the sample is not obey the known Statistical models. And multivariate and small sample problems should be solved in pattern recognition. All of above can't be faultlessly solved by statistical approaches. Generalized statistical pattern recognition method selects proper technique based on the distribution estimation test of samples. It suggests scientific applying the statistical method to pattern recognition and avoiding the distortion and aberration of information because of the statistical method abuse. It also provides a general ways and means of classifier selection which is domain-independent. Firstly, some concerned concepts of special non-statistic were discussed. Then, the process of generalized statistical pattern recognition and non-statistical techniques were introduced. The data experiments of UCI datasets and manual creating dataset based on finite Normal mixture model. The experiment results proved the necessary of generalized statistical pattern recognition.

Original languageEnglish
Pages (from-to)265-273
Number of pages9
JournalAdvances in Information Sciences and Service Sciences
Issue number22
Publication statusPublished - Dec 2012

Scopus Subject Areas

  • Computer Science(all)
  • Mathematics(all)

User-Defined Keywords

  • Finite normal mixture model
  • Generalized statistics
  • Non-statistics
  • Normal distribution
  • Pattern recognition


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