semantic space 中文意思是什麼
semantic space
解釋
語義空間-
Through analyzing several current - prevailing geography information types and gis data formats, this paper proposes a both user - oriented and multi - resource - heterogeneous - information - system - oriented scheme that describes the urban space semantic information entity based on ontology. moreover, ontology - based topology and semantic relationship of the urban space entity are provided. this paper also presents a synthetic query model of ontology - driven space information and a methodology of semantic integration
本文在分析了國內外流行的gis系統的地理信息類型和數據格式的基礎上，提出基於本體論的，面向用戶應用、面向多源異構信息系統的城市空間語義信息實體的描述方法，提出基於本體的城市空間實體拓撲關系及語義關系，確立了本體驅動的空間信息的綜合查詢模型，以及語義信息集成方法。 -
A fuzzy image data model and a concept of fuzzy space are proposed, in which model visual feature, spatial feature and semantic feature are used for super feature in order to utilize advantage of traditional relation database as well as characteristics of image data and fuzzy retrieval. based fuzzy space, a method of similarity measurement of image is presented to support fuzzy features - based image retrieval and satisfy user ' s query requirement for image. in the thesis, a semantic template and the mechanism of dynamic relevant feedback are defined so that it can express user ' s query semantic and improve retrieval precision and useable capability for image retrieval
研究了模糊檢索方法和相關反饋機制在圖象檢索中的應用，提出了一種模糊圖象數據模型和模糊空間的概念，該模型將可視特徵、空間特徵、語義特徵看作超屬性，既充分利用了傳統關系數據庫的優點，同時又考慮了圖象數據以及模糊查詢的特點，文中提出的模糊空間和模糊相似性度量方法能支持基於模糊特徵的圖象查詢，較好地體現用戶圖象查詢的應用需求，文中定義的語義模板和相關反饋機制能在一定程度上表達用戶的查詢語義，提高圖象檢索的準確率和易用性。 -
The feature vector, usedin the vector space model for classification, consists of variousfactors, including the semantic distance from the sentence to the topicand the distance from the sentence to the previous relevant contextoccurring before it
我們分類採用的特徵向量包含多種因素，其中包括當前句子到話題的語義距離以及當前句子到有效上文句子的距離。 -
It includes 3d semantic space and constraining based problem - solving method. the 3d semantic space is built to represent the relationship between the describing semantic word and product color. then, the product color schemes with related abstract sense words is achieved and saved in database
首先採用定性和定量相結合的方法，比較各抽象語義詞匯對人心理感覺影響的差異，建立了抽象語言和色彩關聯的三維風格語義空間，為色彩信息的表示及推理奠定了基礎。 -
The document space is generally of high dimensionality and clustering in such a high dimensional space is often infeasible due to the curse of dimensionality. so the primary step in document clustering is to project the document into a lower - rank semantic space in which the documents related to the same semantics are close to each other
基於文本空間的文本聚類因為其具有高維的特徵而不容易直接實現，所以文本聚類的首要步驟就是將文本空間的數據投影到較低維的語義空間里，使在文本空間里相鄰的數據向量在語義空間里根據某些提取的特徵參數而相似。 -
In this paper we use the color auto - correlogram as the similarity metrics of images in low - level feature space, and change the bandwidth function. then we propose the semantic relevance feedback. the system react differently to the positive and negative user ' s feedback so that the system can go on learning after the annotation process by updating the probabilities of the list of attributes of the relevant images and reaching the real values
本文引入顏色自相關圖特徵作為圖像在底層特徵空間相鄰的度量，並修改了框架中帶寬的計算函數，然後引入反饋機制，對于用戶的正反饋和負反饋分別作不同的處理，以便在使用過程中，系統能夠繼續學習，根據反饋更新圖像的概率鏈表，使之逐漸接近真實情況。 -
After analyzing the principle of keywords and concept retrieval, a new vector space model named sc - vsm based on the semantic concept retrieval was proposed
摘要對關鍵詞和概念檢索的原理進行分析后，提出了一種基於語義概念檢索的向量空間模型以及該模型與關鍵詞檢索結合的檢索方法。 -
The framework suppose that the image ' s semantic feature can be expressed by using a multi - level attribute tree, and the probability of a certain image having a certain attribute can be estimated by interpolation method using the neighboring image ' s value. here, the neighboring relation refers to the neighboring in the low - level feature space
該框架假定圖像的語義特徵可用多層屬性樹來近似表示，並且圖像擁有某個屬性的概率值可由相鄰的圖像的概率值進行插值估計，框架中的相鄰指圖像在底層特徵空間的相鄰。 -
Presently, many scientific research institutes have been investigated english question - answering systems, some mature english question - answering systems have been widely recognized, but few institutes are doing research on chinese question - answering systems, for the chinese question - answering system demands much higher to the correlation domain research request, for example, there is no blank space between chinese words, chinese syntax analysis and semantic understanding is more difficult, all of these have made the chinese question - answering systems development slowly
目前，國外已有很多科研機構參與了英文問答系統的研究，甚至已經有相對成熟的英文問答系統，但是國內參與中文問答系統的研究不多，因為中文問答系統對相關領域的研究要求更高，例如:中文詞語之間沒有空格;漢語的句法分析和語義理解更為困難等，這些都造成了中文問答系統的發展緩慢。 -
This paper discusses the existent typical ranking technologies of term frequency count and hyperlink analysis, in virtue of vector space model, the author proposes a new ranking technique, document similarity ranking, with a basis on similarity of concept - semantic query term
對現存典型的詞頻統計排序技術和超鏈分析排序技術進行了分析，並藉助向量空間模型，提出了一種基於概念語義的查詢詞-文檔相似度排序方法。 -
This paper researches and discusses the theory of latent semantic index, include the theory of single value decompose and word - document matrix. in this paper the author discusses the application of latent semantic index in chinese document clustering based on latent semantic index, researches and discusses vector space model, latent semantic index, electronic dictionary, word - splitting and the algorithm of k - means. this paper presents a improved structure of electronic dictionary and a improved algorithm of word - spliting
本文對潛在語義索引模型進行系統的研究和探討，包括奇異值分解等相關矩陣理論、詞-文檔矩陣等；同時本文研究和探討了潛在語義索引模型在中文文本聚類中的具體應用和實現，包括文本間相似度的度量、詞-文檔矩陣、奇異值分解的具體實現；同時本文對中文文本聚類所涉及的其他一些中文處理技術，包括向量空間模型、電子字典、切詞、 k - means聚類演算法等也進行了研究和探討。 -
Lsi comes from the field of information retrieval. it transforms the original vector space to abstract k - dimension semantic space. so the huge dimensions of the original vector space are reduced greatly, also the training speed and system performance are improved
系統中引入了信息檢索中的常用技術? ?潛在語義索引，把原始向量空間轉換到抽象的k維語義空間，實現原始向量空間的降維，提高網路訓練速度和性能。
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