Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.

针对该问题，采用核典型相关分析方法进行原始特征的二次提取，得到简约而重要的二次特征。

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The method of product feature extraction and analysis can be divided into supervised machine learning methods, semi-supervised machine learning algorithms and unsupervised machine learning algorithm.

产品评价对象的提取与分析的方法主要分为有监督的机器学习方法、半监督的机器学习算法、无监督的机器学习算法。

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Lattice machine is a novel approach to supervised learning.

格机是一种新颖的有监督学习方法。

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