萊斯利·瓦利安特
計算學(xué)習(xí)理論的先驅(qū)
機器學(xué)習(xí)的未來在于開發(fā)能夠從更少的例子中學(xué)習(xí)并更有效地進行歸納的算法。
The design of learning algorithms should be guided by both theoretical insights and practical considerations.
學(xué)習(xí)算法的設(shè)計應(yīng)同時受到理論見解和實踐考慮的指導(dǎo)。
In the context of computational learning, the concept of 'probably approximately correct' (PAC) learning provides a framework for understanding the efficiency and feasibility of learning algorithms.
在計算學(xué)習(xí)的背景下，“可能近似正確”（PAC）學(xué)習(xí)的概念為理解學(xué)習(xí)算法的效率和可行性提供了一個框架。
The success of a learning algorithm depends on its ability to balance between fitting the data and avoiding overfitting.
學(xué)習(xí)算法的成功取決于其在擬合數(shù)據(jù)和避免過擬合之間取得平衡的能力。
Learning is not just about finding patterns, but about understanding the underlying mechanisms that generate those patterns.
學(xué)習(xí)不僅僅是尋找模式，而是理解生成這些模式的潛在機制。
A good learning algorithm should be able to handle noise and uncertainty in the data effectively.
一個好的學(xué)習(xí)算法應(yīng)該能夠有效地處理數(shù)據(jù)中的噪聲和不確定性。
The challenge in learning is not just to memorize, but to generalize from specific examples to broader concepts.
學(xué)習(xí)的挑戰(zhàn)不僅在于記憶，還在于從具體例子中歸納出更廣泛的概念。
In computational learning theory, we seek to understand the fundamental principles that govern learning from data.
在計算學(xué)習(xí)理論中，我們試圖理解從數(shù)據(jù)中學(xué)習(xí)的基本原理。
The ultimate goal of machine learning is to make computers learn from experience and improve their performance over time.
機器學(xué)習(xí)的最終目標(biāo)是讓計算機從經(jīng)驗中學(xué)習(xí)，并隨著時間的推移提高其性能。
The pursuit of knowledge in computer science is a journey through the landscape of logic and creativity.
計算機科學(xué)中的知識追求是一次穿越邏輯和創(chuàng)造力的旅程。
The real test of an algorithm is not just its correctness, but its scalability and robustness.