現代統計模式識別方法以數據滿足一定的統計分布規律為前提，然而現實問題研究中
存在大量分布不理想或小樣本情況。基于多元圖表示的廣義統計模式識別提出基于類別樣本統計分布特性分析來選擇狹義統計或非統計方法，它主張統計學方法在模式識別領域的科學運用。首先介紹了廣義統計模式識別概念，然后基于人工構造數據集對該方法進行了數據仿真實驗，結果顯示，類別樣本統計分布特性差異對分類方法選擇具有顯著影響。
關鍵詞：模式識別，廣義統計，多元可視化，正態分布，有限正態混合模型

Generalized Statistical Pattern Recognition Based on Multivariate Visualization
Gao,Hai-bo1 Xu,Yong-hong1 Hong, Wen-xue1, Cui ,Jian-xin1,2 (1、Dept. of Electrical Engineering, YanShang University, Qinhuangdao 066004,China 2、Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, 066004,China) Abstract： 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. 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. Firstly, some concerned concepts of special non-statistic were
introduced. Then gave the process of generalized statistical pattern recognition. Manual creating the experiment data based on finite normal mixture model, the experiment results show that the error rate of special non-stastical classifier is lower than special statistical classifier for the sample of finite normal mixture model, and there are no distinct difference of normal distribution sample.
Keywords ： Pattern recognition, Generalized statistical, Multivariate visualization,Normal distribution,Finite normal mixture model