这类学习类型的目标不是让效用函数最大化，而是找到训练数据中的近似点。
In this type of learning, the goal is not to maximize a utility function, but simply to find similarities in the training data.
一旦这些不同的函数(例如概率分布)都被掌握，智能体就会很容易判断哪一种行为会让预期效用最大化，并据此选择正确的行为。
Once these different functions (such as the probability distribution) are learned, the correct action to take is simply a matter of deciding which action maximizes the "expected utility" of the agent.
最好的工作点可以被定义为一个最小化或最大化的效用函数。
The best operating point can be defined as the one that minimizes or maximizes the utility function.
基于最大化用户效用函数框架，去掉了以往研究中对效用函数的严格假设，利用粒子群方法设计了分布式速率控制算法。
Restrictive assumptions on utility function are removed and a simple distributed rates algorithm is proposed using the particle swarm optimization based on the network utility maximization framework.
基于最大化用户效用函数框架，去掉了以往研究中对效用函数的严格假设，利用粒子群方法设计了分布式速率控制算法。
Restrictive assumptions on utility function are removed and a simple distributed rates algorithm is proposed using the particle swarm optimization based on the network utility maximization framework.
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