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→自学习 Self learning
Self-learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). It is a learning with no external rewards and no external teacher advices. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.
Self-learning as machine learning paradigm was introduced in 1982 along with a neural network capable of self-learning named Crossbar Adaptive Array (CAA). It is a learning with no external rewards and no external teacher advices. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion.
自学习作为一种机器学习范式，于1982年提出，并提出了一种具有自学习能力的神经网络 叫做'''交叉自适应矩阵 Crossbar Adaptive Array，CAA'''。这是一种没有外部激励和学习器建议的学习方法。CAA自学习算法以交叉方式计算关于行为的决策和关于后果情况的情绪（感觉）。这个系统是由认知和情感的相互作用所驱动的。
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:
The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine:
Receive consequence situation s’;
Receive consequence situation s’;
接受结果状态 s’ ;
Compute emotion of being in consequence situation v(s’);
Compute emotion of being in consequence situation v(s’);
Update crossbar memory w’(a,s) = w(a,s) + v(s’).
Update crossbar memory w’(a,s) = w(a,s) + v(s’).
更新交叉条记忆存储 w’(a，s) w (a，s) + v (s’)。
It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking behavior, in an environment that contains both desirable and undesirable situations.
It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is behavioral environment where it behaves, and the other is genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal seeking behavior, in an environment that contains both desirable and undesirable situations.
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。Caa 存在于两个环境中，一个是行为环境，另一个是遗传环境，从它开始，只有一次接受初始情绪的情况下将遇到的行为环境。在从遗传环境中获得基因组(物种)载体后，CAA 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 ====
==== Feature learning ====