萊斯利·瓦利安特
計算學(xué)習(xí)理論的先驅(qū)
The concept of probably approximately correct (PAC) learning has been foundational in computational learning theory.
可能近似正確（PAC）學(xué)習(xí)的概念在計算學(xué)習(xí)理論中具有基礎(chǔ)性意義。
Understanding the computational complexity of learning problems is essential for developing efficient algorithms.
理解學(xué)習(xí)問題的計算復(fù)雜性對于開發(fā)高效算法至關(guān)重要。
The PAC learning framework provides a formal way to analyze the efficiency and effectiveness of learning algorithms.
PAC學(xué)習(xí)框架提供了一種正式的方法來分析學(xué)習(xí)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.
在機器學(xué)習(xí)中，偏差和方差之間的權(quán)衡對于實現(xiàn)良好的泛化至關(guān)重要。
The complexity of a learning problem is often determined by the size and structure of the hypothesis space.
學(xué)習(xí)問題的復(fù)雜性通常由假設(shè)空間的大小和結(jié)構(gòu)決定。
A good learning algorithm should be able to generalize from a limited set of examples to unseen data.
一個好的學(xué)習(xí)算法應(yīng)該能夠從有限的例子中推廣到未見過的數(shù)據(jù)。
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.
機器學(xué)習(xí)的最終目標是讓機器能夠從經(jīng)驗中學(xué)習(xí)，并隨著時間的推移提高其性能。
The development of robust learning algorithms requires a deep understanding of the underlying data distribution.
開發(fā)魯棒的學(xué)習(xí)算法需要對底層數(shù)據(jù)分布有深刻的理解。
The ability to generalize from limited data is a hallmark of effective learning algorithms.
從有限數(shù)據(jù)中泛化的能力是有效學(xué)習(xí)算法的標志。
The trade-off between bias and variance is a fundamental concept in machine learning.