示例學習 的英文怎麼說

中文拼音 [shìlìxuéxí]
示例學習 英文
learning from examples
  • 示 : Ⅰ動詞（擺出或指出使人知道; 表明） show; indicate; signify; instruct; notify Ⅱ名詞1 [書面語] （給...
  • 學 : Ⅰ動詞1 （學習） study; learn 2 （模仿） imitate; mimic Ⅱ名詞1 （學問） learning; knowledge 2 （學...
  1. Exemplars of learning activities

    學習活動教材示例
  2. The target of the geography research study, primarily the target of experience, emphasizes the study process of students, the student participation directly and experience personally. therefore, combining with the contents and practice that can be proceeded in high school geography teaching, the general mode, cooperative mode, discoverable mode, applying mode and opening mede have been constructed, and illustrated by the active example of " the changes and causes of the phase "

    地理研究性學習的目標主要是體驗性目標，它強調學生的學習過程，學生的直接參與和親身體驗，因此結合中學地理可進行的研究性學習內容的實際，構建了中學地理研究性學習的一般模式、協作模式、發現模式、應用模式和開放模式，並通過「月相變化及成因」的鮮活示例加以說明。
  3. Design and application of a fuzzy classified rule based on learning from fuzzy examples

    基於模糊示例學習的蠓蟲分類規則的設計
  4. 9 blum a, kalai a. a note on learning from multiple - instance examples. machine learning, 1998, 30 : 23 - 29. 10 maron o, lozano - p erez t. a framework for multiple - instance learning

    本文分析了若干具有代表性的多示例學習演算法，揭示出監督學習方法可以轉化為多示例學習方法，只需將學習方法的注意焦點從對示例的區分轉變到對包的區分。
  5. The ids works by two way, misuse detection and anomaly detection, misuse detection flags an intrusion on intrusion signature, this kind of detecting technic can be realized much more easily, and much more accurate, but it can not find some intrusiones that have been disguised or new kinds of intrusion. the anomaly detection can detect in more wide field, anomaly detection can compare new statistic data with average record, then anomaly record will be found, but it ' s more difficult to set a threshold, if the threshold is too big, some intrusion may be put through, if the threshold is too small, the ids will give more false positive alarm, and the threshold will be different with different people or different period, so the ids just simply show us their suspicious record, the administrator or expert will be in duty to analyze this record and give conclusion, the ids give more alarm than it should, leave us more detection record to analyze, and this is a hard work, we can not distinguish an intrusion or not if we analyze only one record, but we can judge if we find the relation among mass detection evidence. in this article, we try distinguish an intrusion using d - s theory ( proof theory ) instead using manual work, the ids will be more helpful and efficient

    濫用檢測採用的是特徵檢測的方法，實現較為簡單，判斷的準確性較高，但是不能判斷一些經過偽裝的入侵或特徵庫中尚未包含的入侵，異常檢測能夠根據以往記錄的特徵平均值，判斷出異常情況，但是對于異常到什麼程度才視為入侵，這個閥值非常難以確定，閥值設定的太高，有可能漏過真正的入侵，如果設定的閥值太低，又會產生較高的誤警率，而且這個閥值因人而異，因時而異，因此現在的入侵檢測系統把這部分異常記錄以一定的形式顯示出來或通知管理人員，交給管理人員去判斷，而這些ids系統難以判斷的記錄，如果對每個證據單獨地進行觀察，可能是難以判斷是否是入侵，而把許多先後證據關聯起來，專家或管理人員根據經驗能夠判斷訪問的合法性，本文試圖引入人工智慧中證據理論的推理策略和示例學習方法，代替人工檢查分析，可以提高效率，降低誤警率，並可以對一個正在進行得可疑訪問實現實時檢測，通過搜索及時判斷，及時阻斷非法訪問，比事後得人工處理更有意義。
  6. Journal of machine learning research, 2004, 5 : 913 - 939. 16 dempster a p, laird n m, rubin d b. maximum likelihood from incomplete data via the em algorithm

    這個一般性的法則揭示了多示例學習與監督學習之間的聯系，為多示例學習演算法的設計提供了一種通用的途徑。
  7. Multi - instance learning from supervised view

    從監督視角進行多示例學習
  8. An experimental study on mathematics learning from example and by doing

    小學生數學示例學習的信息加工過程實驗研究
  9. Changes of representation for efficient learning in structural domains. in proc. the 13th int

    該問題對多示例學習領域的發展起到了推動作用。
  10. You can use the samples to learn about report design, data retrieval, and how to work with the report authoring tools

    您可以使用示例學習如何設計報表、檢索數據以及如何使用報表創作工具。
  11. I introduce the different stages of aop adoption and offer examples of learning applications and guidelines for success at each stage

    我介紹了採用aop的不同階段，並提供了示例學習應用程序和成功完成每個階段的指南。
  12. Then the background theory of wll is given, including constructing and elaborating of condition ( cec ) and lbp ( lfetps based on production )

    文章第二部分接著介紹了示例學習網上課程的理論基礎：基於產生式表示的示例學習和條件建構-優化理論。
  13. In multi - instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags

    在多示例學習中，訓練樣本是由多個示例組成的包，包是有概念標記的，但示例本身卻沒有概念標記。
  14. In this dissertation, after the learning from examples is briefly reviewed, a kind of fuzzy controller based on fuzzy learning from examples is proposed

    本文以示例學習為基礎，針對基本模糊控制器依賴人為經驗的局限性，提出了一種基於模糊示例學習的模糊控制器。
  15. Solving multiple - instance and multiple - part learning problems with decision trees and decision rules. application to the mutagenesis problem. lecture notes in artificial intelligence 2056, stroulia e, matwin s eds.,

    由於本文已經揭示出多示例學習與監督學習之間具有密切聯系，因此本文提出通過建立多示例集成來求解多示例學習問題。
  16. 7 auer p, long p m, srinivasan a. approximating hyper - rectangles : learning and pseudo - random sets. journal of computer and system sciences, 1998, 57 : 376 - 388. 8 auer p. on learning from multi - instance examples : empirical evaluation of a theoretical approach

    經過很多學者的研究，常用的機器學習方法基本上都有了對應的多示例學習版本，但遺憾的是，不同的學習方法在向多示例學習轉化時並沒有一個一般性的方法或法則。
  17. Thirdly, by introducing fuzzy theory into system evaluation, evaluating student, teaching, course resource, and function of whole system. fourthly, making use of learning from examples based on information theory, machine learning algorithm is improved and machine learning decision tree is realized. finally, on reasoning mechanism, combining means of two classes reasoning is taken

    第三，在系統評價中引入了模糊理論，對學生、教學、課程資源以及系統的整體功能進行了評價；第四，採用基於信息論的示例學習，改進了決策樹學習演算法，並建立了機器學習決策樹；第五，在推理機制上，採取兩級推理相結合的方法進行推理，即用基於語義網路的模糊推理確定教學序列，用基於產生式規則的推理確定教學方法，並給出了詳細的推理演算法。
  18. With the deeper research of inductive learning, it ca n ' t meet the automatic acquisition of non - crisp knowledge because of its crisp description. it appears to be very important to research inductive learning in uncertainty condition and therefore the fuzzy extension of traditional id3 - fuzzy id3 is proposed

    隨著歸納學習研究的深入，具有精確描述特徵的示例學習已不能適應一個系統中不精確知識自動獲取的要求，研究不確定環境中的示例學習已非常必要，進而產生了傳統id3演算法的模糊推廣? ?模糊id3演算法。
  19. 3 dietterich t g. machine learning research : four current directions. ai magazine, 1997, 18 : 97 - 136. 4 lindsay r, buchanan b, feigenbaum e, lederberg j. applications of artificial intelligence to organic chemistry : the dendral project

    在提出多示例學習的概念時， t . g . dietterich等人指出，該領域一個非常值得研究的課題是如何對常用的機器學習方法進行改造，使它們可以處理多示例學習任務。
  20. 41 freund y, schapire r e. a decision - theoretic generalization of on - line learning and an application to boosting. lecture notes in computer science 904, vit anyi p m b ed.,

    在一個真實世界基準測試上，多示例集成取得了很好的效果，顯著了提高多種多示例學習器的泛化能力，並獲得了迄今最高的精度記錄。
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