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"The power of machine learning lies in its ability to discover patterns and relationships in data that are not immediately obvious to human observers."

萊斯利·瓦利安特

"The power of machine learning lies in its ability to discover patterns and relationships in data that are not immediately obvious to human observers."

機器學習的力量在于它能夠發現數據中人類觀察者不易立即察覺的模式和關系。

萊斯利·瓦利安特

在機器學習中，目標不僅僅是擬合數據，而是找到一個能夠捕捉數據潛在結構的模型。

"In machine learning, the goal is not just to fit the data, but to find a model that captures the underlying structure of the data."

萊斯利·瓦利安特

"In machine learning, the goal is not just to fit the data, but to find a model that captures the underlying structure of the data."

在機器學習中，目標不僅僅是擬合數據，而是找到一個能夠捕捉數據潛在結構的模型。

萊斯利·瓦利安特

機器學習的成功取決于我們構建的模型中偏差和方差之間權衡的能力。

"The success of machine learning depends on the ability to balance the trade-off between bias and variance in the models we build."

萊斯利·瓦利安特

"The success of machine learning depends on the ability to balance the trade-off between bias and variance in the models we build."

機器學習的成功取決于我們構建的模型中偏差和方差之間權衡的能力。

萊斯利·瓦利安特

計算學習理論中的一個關鍵見解是，學習問題的復雜性由假設空間的復雜性和可用數據的數量決定。

"A key insight in computational learning theory is that the complexity of a learning problem is determined by the complexity of the hypothesis space and the amount of data available."

萊斯利·瓦利安特

"A key insight in computational learning theory is that the complexity of a learning problem is determined by the complexity of the hypothesis space and the amount of data available."

計算學習理論中的一個關鍵見解是，學習問題的復雜性由假設空間的復雜性和可用數據的數量決定。

萊斯利·瓦利安特

機器學習中的挑戰不僅僅是發現數據中的模式，而是發現能夠推廣到新的、未見過的數據的模式。

"The challenge in machine learning is not just to find patterns in data, but to find patterns that generalize to new, unseen data."

萊斯利·瓦利安特

"The challenge in machine learning is not just to find patterns in data, but to find patterns that generalize to new, unseen data."

機器學習中的挑戰不僅僅是發現數據中的模式，而是發現能夠推廣到新的、未見過的數據的模式。

萊斯利·瓦利安特

計算學習理論為理解機器學習算法的能力和局限性提供了一個數學框架。

"Computational learning theory provides a mathematical framework for understanding the capabilities and limitations of machine learning algorithms."