Osmosis
发表于 2025-3-25 04:38:33
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sperse
发表于 2025-3-25 08:31:29
Marilène Dolseving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling
沙文主义
发表于 2025-3-25 14:20:07
Joke Thijssen-Kerstens,Susan Hupkensplementary material: “If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptabl
肉身
发表于 2025-3-25 19:33:37
Jasperien van der Pasch-Fliermanplementary material: “If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptabl
我要沮丧
发表于 2025-3-25 20:42:28
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propose
发表于 2025-3-26 02:39:04
Martine Buscheving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling
curriculum
发表于 2025-3-26 05:01:51
plementary material: “If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptabl
notice
发表于 2025-3-26 09:29:26
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BANAL
发表于 2025-3-26 14:50:02
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史前
发表于 2025-3-26 18:45:15
Susan Hupkenst vector machines are proposed that can effectively deal with the uncertain boundary and improve predictive accuracy in linear SVM for data having uncertainties. This is achieved by dividing the training documents into three distinct regions (positive, boundary, and negative regions) based on a slid