Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy

机译：通过使用原始初始点和二次采样策略获得EM初始点

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The EM algorithm is an efficient algorithm to obtain the ML estimate for incomplete data, but has the local optimality problem. The deterministic annealing EM (DAEM) algorithm was once proposed to solve this problem, which begins a search from the primiti

机译：EM算法是一种获取不完整数据的ML估计的有效算法，但存在局部最优性问题。曾经提出确定性退火EM（DAEM）算法来解决此问题，该算法从原始算子开始搜索

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《Neural Networks, The 2007 IEEE International Joint Conference on》|2007年|P.1115-1120|共6页
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Ishikawa; Y.; Nakano; R.;
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中图分类工业技术;
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annealing; data analysis; expectation-maximisation algorithm; sampling methods; deterministic annealing EM algorithm; expectation-maximization algorithm; incomplete data analysis; maximum likelihood estimation; primitive initial point strategy; subsampling strate;

机译：退火;数据分析;期望最大化算法;抽样方法;确定性退火EM算法;期望最大化算法;不完全数据分析;最大似然估计;原始初始点策略;二次抽样策略;

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4. Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy [C] . Ishikawa, Y., Nakano, Neural Networks, The 2007 IEEE International Joint Conference on . 2007

机译：通过使用原始初始点和二次采样策略获得EM初始点
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机译：获得微分代数方程组的一致初始值集

Obtaining EM Initial Points by Using the Primitive Initial Point and Subsampling Strategy

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