基于高斯噪声模型的马尔可夫网络构建算法

针对小样本集构建稀疏马尔可夫网络计算量大和求解精度不高的问题，提出一种基于高斯噪声模型的迭代噪声消减（iterative noise reduction，INR）算法。该算法首先利用回归误差的高斯特性筛选相关变量，然后通过boosting方法的自回归更新策略逐步改进学习能力，最后采用赤池信息准则（Akaike information criterion，AIC）避免出现过拟合。此外，给出了自回归更新公式，实现了可控的学习错误率并分析了计算复杂度。实验结果表明，INR能有效构建高维稀疏网络，在学习效率和精度方面具有明显优势。

Gaussian noise model based algorithm to construct Markov network

To solve the difficulties of high calculation quantity and low precision in constructing sparse Markov network with a small set of samples,an iterative noise reduction(INR) algorithm based on the Gaussian noise model is proposed.The algorithm firstly picks out the related variables through employing statistic test to regression residuals.After that,a learning ability is gradually improved through the autoregressive update strategy similar as boosting method.Finally,Akaike information criterion(AIC) is used to avoid overfit.In addition,the iterative update formula is provided and the error rate controlling is realized.Furthermore,the computational complexity of the proposed algorithm is analyzed.The experimental results show that INR can effectively construct the high dimensional sparse network and has obvious advantages on learning precision and efficiency.