萊斯利·瓦利安特
計算學習理論的先驅(qū)
偏差和方差之間的權(quán)衡是機器學習中的一個基本概念。
The success of a learning algorithm depends on the quality and quantity of the data it is trained on.
學習算法的成功取決于其訓練數(shù)據(jù)的質(zhì)量和數(shù)量。
The study of learning algorithms must consider both the statistical and computational aspects of learning.
學習算法的研究必須同時考慮學習的統(tǒng)計和計算方面。
The concept of computational efficiency is central to the design of learning algorithms.
計算效率的概念是學習算法設計的核心。
The PAC learning framework provides a formal way to quantify the learnability of a concept class.
PAC學習框架提供了一種形式化的方法來量化概念類的可學習性。
A key insight in learning theory is that the complexity of a hypothesis class is crucial for generalization.
學習理論中的一個關鍵見解是，假設類的復雜性對于泛化至關重要。
The challenge in computational learning theory is to understand the capabilities and limitations of learning algorithms.
計算學習理論中的挑戰(zhàn)在于理解學習算法的能力和局限性。
The ultimate goal of machine learning is to make computers learn from experience and improve their performance on tasks over time.
機器學習的最終目標是讓計算機從經(jīng)驗中學習，并隨著時間的推移提高其在任務上的表現(xiàn)。
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