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well-founded的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列特價商品、必買資訊和推薦清單
well-founded的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦Koehler, Bodo寫的 The Basics of Life: Metabolism and Nutrition 和Moss, Lawrence S. (EDT)的 Papers in Honor of Jon Barwise都 可以從中找到所需的評價。
這兩本書分別來自 和所出版 。
元智大學 工業工程與管理學系 蔡啟揚所指導 謝諦文的 預測方法在緩慢移動的需求項目中的應用：案例研究 （2021），提出well-founded關鍵因素是什麼，來自於預測、緩慢移動的需求、Croston方法、聚集-分解。
而第二篇論文中原大學 應用數學系 孫天佑所指導 卓瑞發的 標示錯誤的資料對模型機率校正的影響 （2021），提出因為有 機器學習、機率校正、標示錯誤的資料、K-鄰近演算法的重點而找出了 well-founded的解答。
The Basics of Life: Metabolism and Nutrition
為了解決well-founded 的問題，作者Koehler, Bodo 這樣論述：
What are the foundations of life, and what is Life-supporting Medicine? To sustain life and support life processes, nature makes tremendous efforts. If something has gone wrong in the organism and illness occurs, it is never a trifle, but a fundamental disorder. This indicates complicated interrelat
ionships. They are for the most part unexplored. Despite everything, there are always very simple principles that need to be recognised. This book identifies such principles, from which often amazingly simple guidelines for nutrition and medical treatment can be derived. However, it is crucial that
no suppressive and destructive measures are used, but rather supportive, integrating methods. The author broadens the horizon with well-founded scientific research results, which lead to completely new insights and enable a different, life-supporting view of the human being. The author, Dr Bodo Koeh
ler, MD, born in 1948, is an internist with extensive additional training in naturopathic medicine and has almost 50 years of experience in clinics and his own practice. Through intensive research work and active exchange with many top-class scientists, he has acquired an extensive range of knowledg
e. This has resulted in several specialist books and over 150 publications as well as his own therapy methods and the development of medical devices. The author is active as a lecturer at home and abroad.
well-founded進入發燒排行的影片
預測方法在緩慢移動的需求項目中的應用：案例研究
為了解決well-founded 的問題，作者謝諦文 這樣論述：
慢速需求項目之所以被稱為慢速，是因為它們在需求規模或發生週期上呈現出不規則性，因此傳統的預測方法通常顯示出不准確的結果，將它們劃分為不穩定、間歇性和不穩定的需求項目。本研究考察了預測方法在哥倫比亞研究中心項目需求中的應用，這些方法是基於指數平滑法的，但具體到這些類型，如Croston方法及其修改，SBA和SBJ，以及更多目前側重於時間序列聚集和分解的模型（ADIDA和MAPA）。同樣，通過審查平均絕對誤差（MAE）和平均平方誤差（RMSE）來評估每種方法的準確性，以便就哪種類型的方法對某一類型的緩慢移動需求更準確提供一個有根據的建議。
Papers in Honor of Jon Barwise
為了解決well-founded 的問題，作者Moss, Lawrence S. (EDT) 這樣論述：
Jon Barwise (1942-2000) was a noted scholar of mathematical logic and philosophy who served on the faculties of Yale University, the University of Wisconsin, Stanford University (where he was cofounder and the first director of the Center for the Study of Language and Information), and Indiana Un
iversity. This collection honors Barwise's legacy to the academy with current contributions inspired by his diverse fields of interest, from infinitiary logic to natural language, situation semantics, circular claims, and non-well-founded set theory.
標示錯誤的資料對模型機率校正的影響
為了解決well-founded 的問題，作者卓瑞發 這樣論述：
機器學習中任何分類器的準確率，都取決於資料的品質，而資料的準確性、完整性和一致性這三大點是決定資料質量的重要因素。在資料中或多或少會有標示錯誤的資料，故本研究將探討標示錯誤的資料對模型機率校正的影響。本論文我們會先從機率校正的理論開始，分別討論Platt Scaling、Isotonic Regression、Beta Calibration與Spline Calibration四種機率校正方法。我們分別探討對汙染10%、15%和20%資料量的資料對模型機率校正的影響。最後我們會利用K-鄰近演算法（KNN演算法）偵測資料是否汙染，並且修復標示錯誤的資料，探討改正後的資料對模型機率校正的影響。