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61.
针对当前尚无建立简约高效语音识别系统标准方法的情形,提出了通过贝叶斯信息准则(Bayesian Information Criterion,BIC)中的权衡系数折中选择系统识别率与复杂度,利用改进的粒子群优化(Particle Swarm Optimization,PSO)算法优化声学模型拓扑结构,进而创建高效简约语音识别系统的新方法。TIDigits上的实验表明,与传统方法创建的同复杂度的基线系统相比,用该方法建立的新系统句子正确率提升了7.85%,与同识别率的基线系统相比,系统复杂度降低了51.4%,说明新系统能够以较低的复杂度获得较高的识别率。 相似文献
62.
大跨度桥梁风荷载模拟及程序编制 总被引:7,自引:0,他引:7
提出了一种有效而实用的风荷载模拟方法.利用工程实际中普遍采用的风速谱,采用多维自回归模型AR,并应用BIC法则确定多维自回归模型的阶数,使模型与功率谱的拟合更具相合性.用VB语言模拟了一座跨度为1000m的悬索桥的主梁随机风场,验证了该方法的有效性. 相似文献
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64.
Process monitoring using multistage processes with binary data remains an important and challenging problem in statistical process control (SPC). Although the multistage processes has been extensively studied in the literature, the challenges associated with designing diagnostic schemes when only binary responses are observed are yet to be addressed well. This paper develops a practical LASSO-based diagnostic procedure which combines BIC with the popular adaptive LASSO variable selection method. Given the oracle property of LASSO and its algorithm, the diagnostic result can be obtained easily and quickly. More importantly, the proposed method does not require making any extra tests that are necessary in existing diagnosis methods. Numerical and real-data examples demonstrate the effectiveness of our method. 相似文献
65.
Abstract. In linear regression models with autocorrelated errors, we apply the residual likelihood approach to obtain a residual information criterion (RIC), which can jointly select regression variables and autoregressive orders. We show that RIC is a consistent criterion. In addition, our simulation studies indicate that it outperforms heuristic selection criteria – the Akaike information criterion and the Bayesian information criterion – when the signal-to-noise ratio is not weak. 相似文献
66.
针对居民日用电负荷的聚类分析和预测问题提出了一种基于居民用电负荷模式精细分类的预测框架。 为了提高用于
聚类分析的特征质量,首先基于贝叶斯信息准则(BIC)实现特征筛选。 然后,采用基于加权皮尔逊距离的密度峰值法实现居民
用电负荷曲线形态的准确识别。 接下来,通过融合激活函数的方法对长短期记忆(LSTM)预测网络进行改进。 最后,利用改进
后的 LSTM 网络对精细分类的居民用电负荷模式进行预测。 实验结果表明,根据所提出的方法得到的预测误差指标为平均绝
对百分误差(MAPE),MAPE= 6. 6792%,提高了负荷预测质量,在居民用电负荷预测中具有较好的效果。 相似文献
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In survival analysis, it is of interest to appropriately select significant predictors. In this paper, we extend the AICC selection procedure of Hurvich and Tsai to survival models to improve the traditional AIC for small sample sizes. A theoretical verification under a special case of the exponential distribution is provided. Simulation studies illustrate that the proposed method substantially outperforms its counterpart: AIC, in small samples, and competes it in moderate and large samples. Two real data sets are also analyzed. 相似文献
69.
Andreas Baierl Andreas Futschik Przemys?aw Biecek 《Computational statistics & data analysis》2007,51(12):6423-6434
One of the most popular criteria for model selection is the Bayesian Information Criterion (BIC). It is based on an asymptotic approximation using Bayes rule when the sample size tends to infinity and the dimension of the model is fixed. Although it works well in classical applications, it performs less satisfactorily for high dimensional problems, i.e. when the number of regressors is very large compared to the sample size. For this reason, an alternative version of the BIC has been proposed for the problem of mapping quantitative trait loci (QTLs) considered in genetics. One approach is to locate QTLs by using model selection in the context of a regression model with an extremely large number of potential regressors. Since the assumption of normally distributed errors is often unrealistic in such settings, we extend the idea underlying the modified BIC to the context of robust regression. 相似文献
70.
Yali LV Junzhong MIAO Jiye LIANG Ling CHEN Yuhua QIAN 《Frontiers of Computer Science》2021,15(6):156337
Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithmbased on the frequently used Bayesian information criterion (BIC) score function. The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective. Specifically, we first find the most dependent node for each individual node, prove analytically that the dependencies are undirected, and then construct undirected subgraphs UG. Secondly, the UG- is examined and connected into a single undirected graph UGC. The relation between the subgraph number and the node number is analyzed. Thirdly, we provide the rules of orienting directions for all edges in UGC, which converts it into a directed acyclic graph (DAG). Further, we rank the DAG’s topology order and describe the BIC-based node order learning algorithm. Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples, and in polynomial time with respect to the number of variables. Finally, experimental results demonstrate significant performance improvement by comparing with other methods. 相似文献