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Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

机译：主成分分析和正交最小正方形的优化特征在分类中使用支持向量机进行分类

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R. Sahak; W. Mansor; Khuan Y. Lee; A. Zabidi;
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年度 2018
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1. OPTIMIZATION OF PRINCIPAL COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINE FOR THE RECOGNITION OF INFANT CRY WITH ASPHYXIA [J] . Rohilah Sahak, Wahidah Mansor, Khuan Y. Lee, International Journal of Computers & Applications . 2013,第3期

机译：用于窒息婴儿哭泣识别的主成分分析和支持向量机优化。
2. Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump [J] . Transactions of the Institute of Measurement and Control . 2020,第6期

机译：基于内核主要成分分析的最小二乘支持向量机通过改进的灰羽优化算法优化，以及在梁泵动态液位预测中的应用
3. Sparse data-dependent kernel principal component analysis based on least squares support vector machine for feature extraction and recognition [J] . Jun-Bao Li, Huijun Gao Neural computing & applications . 2012,第8期

机译：基于最小二乘支持向量机的稀疏数据相关核主成分分析用于特征提取与识别
4. Orthogonal least square and optimized support vector machine with polynomial kernel for classifying asphyxiated infant cry [C] . Sahak R., Mansor W., Lee Y. K., 2011 IEEE International Conference on Signal and Image Processing Applications . 2011

机译：具有多项式核的正交最小二乘优化支持向量机，用于对窒息性婴儿啼哭进行分类
5. Processability Analysis using Principal Component Analysis and Support Vector Machine. [D] . Zhang, Yixin. 2014

机译：使用主成分分析和支持向量机的可加工性分析。
6. Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier [O] . Mien Van, Duy Tang Hoang, Hee Jun Kang 2020

机译：基于粒子群算法的最小二乘支持向量机分类器轴承故障诊断
7. Indefinite Kernels in Least Squares Support Vector Machines and Principal Component Analysis [O] . Huang Xiaolin, Maier A, Hornegger J, 2017

机译：最小二乘支持向量机中的不定核和主成分分析

Performance of Principal Component Analysis and Orthogonal Least Square on Optimized Feature Set in Classifying Asphyxiated Infant Cry Using Support Vector Machine

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