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→特征学习 Feature learning
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering.<ref name="coates2011">{{cite conference
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering.<ref name="coates2011">{{cite conference
|last1 = Coates
|last1 = Coates
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特征学习可以是有监督的，也可以是无监督的。在有监督的特征学习中，可以利用标记输入数据学习特征。例如'''人工神经网络 Artificial neural networks，ANN'''、'''多层感知机 Multilayer perceptrons，MLP'''和受控字典式学习模型 Supervised dictionary learning model，SDLM。在无监督的特征学习中，特征是通过未标记的输入数据进行学习的。例如，'''字典学习 Dictionary learning'''、'''独立元素分析 Independent component analysis'''、'''自动编码器 Autoencoders'''、'''矩阵分解 Matrix factorization'''和各种形式的聚类