document classification造句
例句與造句
- Study of ontology - based document classification system
基于本體的文檔自動分類系統(tǒng)的研究 - On the quality of document classification staffs in 21st century
論新時期文獻(xiàn)分類人員的素養(yǎng) - As a result , rules for web documents classification are produced
最后，推導(dǎo)出web文本分類的規(guī)則集。 - Automatic document classification
自動文件分類 - In addition to the document classification algorithm , a web - based document classification system is also developed and a demonstration case is applied to verify the performance of the proposed approach
綜合言之，本研究之目標(biāo)乃為提升文件自動分類技術(shù)之正確率與效率性，以協(xié)助企業(yè)組織與機(jī)構(gòu)有效提高其知識文件管理之效能，并進(jìn)而提升企業(yè)之知識利用率。 - It's difficult to find document classification in a sentence. 用document classification造句挺難的
- In order to enhance the performance for enterprises to manage their digital documents and domain knowledge , automatic document classification has become a key issue for enterprise knowledge management
由于電子化文件之內(nèi)容充滿復(fù)雜性與多樣性，故以人工決策之方式判斷文件類別不僅不符合經(jīng)濟(jì)效益且其處理速度亦十分緩慢；此外，文件類別認(rèn)定標(biāo)準(zhǔn)亦難維持一致性。 - The attempt of this research is to enhance the accuracy and efficiency of enterprise document classification technology and to enable a self - service knowledge management mechanism in organizations
此外，對于資訊需求者而言，本研究則能協(xié)助資訊需求者于龐大之網(wǎng)路資訊/文件中，迅速且便捷地尋得其所需要之文件資料，以節(jié)省資訊需求者花費于資訊過濾與篩選之大量時間。 - This method is suitable to automatic construction of ontology of all the other domains and valuable to applications . the web document classification system combining the fuzzy neural network with the concept hierarchy of ontology is developped
此方法適用于構(gòu)造所有領(lǐng)域的本體論，具有較大的實用價值；設(shè)計一個將模糊神經(jīng)網(wǎng)絡(luò)與本體論的概念層次結(jié)構(gòu)相結(jié)合的web文檔分類系統(tǒng)。 - This paper comprises the discussion of intel ligent agent , natural language processing , document representation , document classification , support vector machine , and the detailed design and implementation of web news hunter intelligent agent system
本文包括對智能代理、自然語言處理、文本表示、網(wǎng)絡(luò)搜索、文本分類和支持向量機(jī)等網(wǎng)絡(luò)挖掘相關(guān)領(lǐng)域的理論、算法和應(yīng)用的探討，以及webnewshunter智能代理的系統(tǒng)框架的設(shè)計與實現(xiàn)。 - Content analysis and document classification must follow the logical rules of classification , but the classification of contents from micro - degree ; it is good for getting competitive information by content analysis ; it is good for strengthening the reliability and accuracy of book review and improving review value and academic value
內(nèi)容分析法與文獻(xiàn)分類都必須遵守分類的邏輯原則，但內(nèi)容分析的分類從微觀角度透視各種載體內(nèi)容；運用內(nèi)容分析有利于獲得競爭情報；有利于增強(qiáng)書評的客觀性精確性，提高評論價值與學(xué)術(shù)價值。 - With difference from the way that traditional methods perform by accumulating the frequency of keywords . we propose a new metrical function that employs the rs - based entropy by comparing function values to measure the feature of web pages . besides , according to the unstructured and heterogenous characteristics of www , the effect of hypertext tags to keywords " weigh has been taken into account to obtain the most effective keywords for document classification
有別于傳統(tǒng)的對關(guān)鍵字頻度進(jìn)行累加的方法，本文提出了基于信息熵的文本關(guān)鍵詞測度函數(shù)，通過對關(guān)鍵詞函數(shù)值進(jìn)行比較，獲取對文本分類最具影響性的關(guān)鍵詞序列；同時，針對web上異質(zhì)、非結(jié)構(gòu)化信息的特點，該分類算法還考慮了超文本標(biāo)記對關(guān)鍵詞權(quán)值的影響。 - In this paper , a rs - based model , which starts up from trained documents for web documents classification , is introduced . taking advantage of the rs theory ' s features in efficient dealing with vague , indiscernible , and fuzzy information , we sets up a series of layered subsystems to reduce redundant properties from classification tables . in this way , we can not only efficiently reduce the dimension of documents but also keep the information of keywords set
本文提出了一種基于粗糙集理論的web文本分類模型，該模型從已分類的訓(xùn)練文本出發(fā)，建立一系列不同層次的文本分類子系統(tǒng)，利用roughset理論有效處理不精確、不確定、含糊信息的特性，對分類決策表進(jìn)行屬性約簡，既有效降低了web文本的維度，又保持關(guān)鍵詞集合中的信息。