模式收獲表 的英文怎麼說
中文拼音 [móshìshōuhuòbiǎo]
模式收獲表
英文
normal yield table- 模 : 模名詞1. （模子） mould; pattern; matrix 2. （姓氏） a surname
- 式 : 名詞1 （樣式） type; style 2 （格式） pattern; form 3 （儀式; 典禮） ceremony; ritual 4 （自然科...
- 收 : Ⅰ動詞1 （把攤開的或分散的事物聚集、合攏） put away; take in 2 （收取） collect 3 （收割） harvest...
- 獲 : Ⅰ動詞1. （捉住; 擒住） capture; catch 2. （得到;取得） obtain; win; reap 3. （收割） reap; harvest Ⅱ名詞（姓氏） a surname
- 表 : Ⅰ名詞1 （外面;外表） outside; surface; external 2 （中表親戚） the relationship between the child...
- 模式 : model; mode; pattern; type; schema
- 收獲 : 1. （取得成熟的農作物） gather in the crops; harvest; reap 2. （比喻心得、戰果等） results; gains
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During the procedure of system design and implementation, the author has made some innovative efforts such as : ( d establishing the user interest orientated model, the model receiving user interests continuously and conjecturing user interests by interaction with the user, accumulating user preferences in information demand, thereby achieving self - adaptive retrieval, ? roviding a feedback method which is based on the human - machine interaction, summarizing the user operations on the interface of result presentation, and designing an algorithm for capturing user operation behaviors, by which the changes in user interests and preferences can be learned potentially, ? ffering a method for user interest mining which can extract subjects of information confirmed by user, thereby conjecturing or predicting different kinds of expressions of the same interest or extracting the new interests or unexpressed interests, ? roposing a solution of personalized internet information retrieval based on the user interests in accordance with the above - mentioned work, the solution having very strong feasibility and practicality with taking user interest model as center, employing machine learning ( active learning and passive learning ) and data mining as tools, and being assisted with network robot,
Piirs系統分析與設計過程中所做的創新性的嘗試主要有以下幾個方面：實現了基於用戶興趣的用戶模型，該模型通過與用戶的交互（主動交互和被動交互） ，不斷地接收用戶的興趣和推測用戶的興趣，積累用戶信息需求的偏好，實現自適應的檢索；提供了一種基於人機交互的反饋方法，對用戶在結果呈現界面上的操作進行了歸納總結，設計了用戶操作捕獲演算法， 「隱性地」學習用戶興趣和偏好的變化；提供了一種用戶需求挖掘的方法，對用戶已確定的信息做進一步的主題挖掘，由此推測或預測用戶同一興趣的不同表述方式或者挖掘出用戶新的或未表達出來的興趣；在上述工作基礎上提出了一套完整的基於用戶興趣的個性化網路信息檢索的解決方案，該方案以用戶興趣模型為中心，以機器學習（主動學習和被動學習）和數據挖掘為手段，輔以網路機器人，具有很強的可行性和實用性。
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