The concept of probably approximately correct (PAC) learning has been foundational in computational learning theory.
可能近似正确（PAC）学习的概念在计算学习理论中具有基础性意义。
Understanding the computational complexity of learning problems is essential for developing efficient algorithms.
理解学习问题的计算复杂性对于开发高效算法至关重要。
The PAC learning framework provides a formal way to analyze the efficiency and effectiveness of learning algorithms.
PAC学习框架提供了一种正式的方法来分析学习算法的效率和有效性。
In machine learning, the trade-off between bias and variance is crucial for achieving good generalization.
在机器学习中，偏差和方差之间的权衡对于实现良好的泛化至关重要。
The complexity of a learning problem is often determined by the size and structure of the hypothesis space.
学习问题的复杂性通常由假设空间的大小和结构决定。
A good learning algorithm should be able to generalize from a limited set of examples to unseen data.
一个好的学习算法应该能够从有限的例子中推广到未见过的数据。
The challenge in computational learning theory is to understand the capabilities and limitations of learning algorithms.
The ultimate goal of machine learning is to make machines that can learn from experience and improve their performance over time.
机器学习的最终目标是让机器能够从经验中学习，并随着时间的推移提高其性能。
The development of robust learning algorithms requires a deep understanding of the underlying data distribution.
开发鲁棒的学习算法需要对底层数据分布有深刻的理解。
The ability to generalize from limited data is a hallmark of effective learning algorithms.
从有限数据中泛化的能力是有效学习算法的标志。
The trade-off between bias and variance is a fundamental concept in machine learning.