萊斯利·瓦利安特
計(jì)算學(xué)習(xí)理論的先驅(qū)
"The ultimate test of a machine learning algorithm is its ability to perform well on new, unseen data."
機(jī)器學(xué)習(xí)算法的最終測試是它在新的、未見過的數(shù)據(jù)上表現(xiàn)良好的能力。
"The future of machine learning will depend on our ability to develop algorithms that can learn from complex, high-dimensional data."
機(jī)器學(xué)習(xí)的未來將取決于我們開發(fā)能夠從復(fù)雜、高維數(shù)據(jù)中學(xué)習(xí)的算法的能力。
"One of the most important challenges in machine learning is to develop algorithms that can learn from small amounts of data."
機(jī)器學(xué)習(xí)中最重要的挑戰(zhàn)之一是開發(fā)能夠從少量數(shù)據(jù)中學(xué)習(xí)的算法。
"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(jī)器學(xué)習(xí)的力量在于它能夠發(fā)現(xiàn)數(shù)據(jù)中人類觀察者不易立即察覺的模式和關(guān)系。
"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(jī)器學(xué)習(xí)中，目標(biāo)不僅僅是擬合數(shù)據(jù)，而是找到一個(gè)能夠捕捉數(shù)據(jù)潛在結(jié)構(gòu)的模型。
"The success of machine learning depends on the ability to balance the trade-off between bias and variance in the models we build."
機(jī)器學(xué)習(xí)的成功取決于我們構(gòu)建的模型中偏差和方差之間權(quán)衡的能力。
"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(jì)算學(xué)習(xí)理論中的一個(gè)關(guān)鍵見解是，學(xué)習(xí)問題的復(fù)雜性由假設(shè)空間的復(fù)雜性和可用數(shù)據(jù)的數(shù)量決定。
機(jī)器學(xué)習(xí)中的挑戰(zhàn)不僅僅是發(fā)現(xiàn)數(shù)據(jù)中的模式，而是發(fā)現(xiàn)能夠推廣到新的、未見過的數(shù)據(jù)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."
機(jī)器學(xué)習(xí)中的挑戰(zhàn)不僅僅是發(fā)現(xiàn)數(shù)據(jù)中的模式，而是發(fā)現(xiàn)能夠推廣到新的、未見過的數(shù)據(jù)的模式。
"Computational learning theory provides a mathematical framework for understanding the capabilities and limitations of machine learning algorithms."
計(jì)算學(xué)習(xí)理論為理解機(jī)器學(xué)習(xí)算法的能力和局限性提供了一個(gè)數(shù)學(xué)框架。
機(jī)器學(xué)習(xí)的最終目標(biāo)是制造能夠從經(jīng)驗(yàn)中學(xué)習(xí)并隨時(shí)間提高性能的機(jī)器。
The future of machine learning lies in the development of algorithms that can learn from fewer examples and generalize more effectively.
- «
- »