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Titlebook: Maritime Archaeology; A Reader of Substant Lawrence E. Babits,Hans Tilburg Book 1998 Springer Science+Business Media New York 1998 Underwat

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樓主: CLIP
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發(fā)表于 2025-3-23 13:10:36 | 只看該作者
Introductionan trying to explain change (Binford 1972: 2–13). The “new archaeologists” utilized an explicitly evolutionary approach, and they proposed scientific methodology for examining their data and its interpretation.
12#
發(fā)表于 2025-3-23 17:53:36 | 只看該作者
13#
發(fā)表于 2025-3-23 21:17:23 | 只看該作者
Book 1998 excitement. It is appropriate that the first book of the series is a collection of articles intended for gradu- ate or undergraduate courses in underwater archaeology, since the growth in academic opportunities for students is an important sign of the vitality of this subdiscipline. The layman will
14#
發(fā)表于 2025-3-24 00:10:05 | 只看該作者
ear, in the UK, there were almost two million hospital consultations for urological symptoms and an estimated fve million consultations in general practice. With such a signifcant proportion of care being delivered for urology, 80% of which can be managed medically, we felt that a resource should be
15#
發(fā)表于 2025-3-24 04:00:32 | 只看該作者
16#
發(fā)表于 2025-3-24 10:06:05 | 只看該作者
e UK, there were almost two million hospital consultations for urological symptoms and an estimated fve million consultations in general practice. With such a signifcant proportion of care being delivered for urology, 80% of which can be managed medically, we felt that a resource should be made avai
17#
發(fā)表于 2025-3-24 10:58:18 | 只看該作者
18#
發(fā)表于 2025-3-24 18:02:14 | 只看該作者
Introduction, Colorado. Building upon the work of James B. Griffin, Albert Spaulding, Julian Steward, W. W. Taylor, and Leslie White, Lewis Binford and others began trying to explain change (Binford 1972: 2–13). The “new archaeologists” utilized an explicitly evolutionary approach, and they proposed scientific
19#
發(fā)表于 2025-3-24 21:26:54 | 只看該作者
Argyroula Kalaitzaki,Sean Hammondhis problem challenging. Ensemble methodologies have evolved to leverage potentially thousands of base classifiers that are usually instantiations of the same underlying model (e.g., neural networks, decision trees). Random forest (RF) is a classical ensemble classification algorithm which gives the
20#
發(fā)表于 2025-3-25 00:28:49 | 只看該作者
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