覆盖算法的概率模型

Probabilistic Model for Covering Algorithm

要从本质上提高覆盖算法的精度,必须在算法中引入全局的优化计算.为此,先将覆盖算法扩展成核覆盖算法(以高斯函数为核函数),再利用高斯函数的概率意义(高斯分布),为核覆盖算法建立一个有限混合概率模型,在此基础上,利用"最大似然原理"引入全局优化计算,并利用EM(expectation maximization)方法进行求解,完成对覆盖算法的全局优化计算,从而扩大覆盖方法的使用范围并提高算法的精度,且将它从确定的模型扩展成概率的模型,后者更具抗噪声干扰的能力.最后给出模拟实验,实验比较结果表明,经优化后的概率模型确实提高了算法的精度.

It is necessary to bring global optimization in covering algorithm to improve its precision of classification. So a probabilistic model of covering algorithm is put forward in this paper. Firstly, the covering algorithm is ameliorated to kernel covering model （Gaussian function is the kernel function）, then a kind of finite mixture probabilistic model for kernel covering model is introduced according to the probabilistic meaning of Gaussian function. Finally, the global optimization calculation is inducted based on maximum likelihood theory and Expectation Maximization Algorithm. Therefore, the algorithm optimizes the covering network broadens the application domain of covering algorithm and improves its robustness. The experimental results show that the optimized probabilistic model of covering algorithm can improve the accuracy of classification.