更改

它是一个只有一个输入、情景和一个输出、动作(或行为)的系统。既没有单独的强化输入，也没有来自环境的通知输入。反向传播价值（二次强化）是对结果情境的情感信息。CAA 存在于两种环境中，一种是行为环境，另一种是遗传环境，CAA将从这样的环境中获取且仅获取到一次关于它自身的初始情绪（这种情绪信息描述了算法应该对这样环境下对应的结果持有何种态度）。在从遗传环境中获得基因组(物种)载体后，CAA 会在一个既包含理想情况又包含不理想情况的环境中学习一种寻求目标的行为。

它是一个只有一个输入、情景和一个输出、动作(或行为)的系统。既没有单独的强化输入，也没有来自环境的通知输入。反向传播价值（二次强化）是对结果情境的情感信息。CAA 存在于两种环境中，一种是行为环境，另一种是遗传环境，CAA将从这样的环境中获取且仅获取到一次关于它自身的初始情绪（这种情绪信息描述了算法应该对这样环境下对应的结果持有何种态度）。在从遗传环境中获得基因组(物种)载体后，CAA 会在一个既包含理想情况又包含不理想情况的环境中学习一种寻求目标的行为。

==== Feature learning ====

==== 特征学习 Feature learning ====

{{Main|Feature learning}}

{{Main|Feature learning}}

Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal components analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task.

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

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

 

 

==== Sparse dictionary learning ====

==== Sparse dictionary learning ====