基于确定性退火技术的分类器设计

Classifier Design Based on Deterministic Annealing

针对传统分类器的缺陷,文章提出将分类问题看作一物理系统,把求解分类问题的最优解转化为模拟温度变化的物理系统的平衡态,通过求解一系列温度变化的物理系统自由能函数的极小来模拟物理系统的平衡态,最终达到物理系统的基态,即分类问题的最优解。由确定性退火技术构造学习规则用于优化分类器参数,目的是减少分类误差以及待识别空间的系统熵。试验表明,将确定性退火技术用于分类器设计中,降低了分类误差,取得了良好的分类性能。

In contrast with the defects of traditional classifiers,this paper presents a viewpoint which looks upon the classification problem as a physical system,thus transforming the optimization of a classification problem into the equilibrium of a physical system simulating the temperature fluctuations.By solution of the minimum of a set of free energy functions of the physical system simulating temperature fluctuations,the base state of the physical system is ultimately reached,which corresponds to the optimum solution of the classification problem.Learning rules are constructed according to deterministic annealing to optimize classifier parameters,on purpose to reduce classification error and system entropy of the space to be identified.The experimental results show that,by adopting deterministic annealing,the classification error is greatly reduced and hetter classification is obtained.