本文選題：概念 + 模糊語義細(xì)胞��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：概念實(shí)體的表達(dá)往往具有一定的模糊性,這種模糊性是蘊(yùn)含在在語義中出現(xiàn)的。使用合適的概念模型來表達(dá)模糊語義具有重要的意義。模糊語義細(xì)胞作為最小的模糊概念的表示單元,在數(shù)據(jù)挖掘、機(jī)器學(xué)習(xí)以及知識(shí)發(fā)現(xiàn)中具有重要的作用。在概念空間(論域)Ω上,模糊語義細(xì)胞L =P,d,δ被稱為"關(guān)于pi","類似Pi"以及"和Pi接近"的語義標(biāo)簽,其中P代表概念i的原型,d是定義在論域Ω上的距離函數(shù),δ則是概念空間中定義在[0,+∞)上其他點(diǎn)和Pi的距離的概率密度函數(shù)。在模糊語義細(xì)胞的學(xué)習(xí)中我們需要關(guān)注語義的覆蓋程度、描述的清晰程度以及描述的模糊性這三個(gè)因素,因此模糊語義細(xì)胞的學(xué)習(xí)原則很自然地就聯(lián)系到最大覆蓋率、最具典型性和最大模糊熵這三個(gè)指標(biāo)之上。本文中混合模糊語義細(xì)胞是建立在模糊語義細(xì)胞的學(xué)習(xí)基礎(chǔ)之上,模糊語義細(xì)胞學(xué)習(xí)的最終目標(biāo)是要尋找最佳的L來刻畫具有某個(gè)概念的數(shù)據(jù)集,而混合模糊語義細(xì)胞則在此基礎(chǔ)上做了更深一層的拓展,考慮具有若干個(gè)相關(guān)的概念的集合LA={L1,L2,...,Ln},其中每個(gè)概念都對(duì)應(yīng)使用模糊語義細(xì)胞Li來描述第i個(gè)概念的數(shù)據(jù)集,混合模糊語義細(xì)胞的學(xué)習(xí)是為了能夠?qū)ふ业揭唤M最合適的權(quán)重參數(shù)W={W1,w2,...w2}來刻畫某個(gè)概念在此概念集合(主題)中的影響程度或者是重要程度,借鑒之前的模糊語義細(xì)胞的學(xué)習(xí)原則,我們需要重新定義并計(jì)算語義細(xì)胞的兩個(gè)數(shù)字特征:期望粒度R和模糊熵H。最終將學(xué)習(xí)混合模糊語義細(xì)胞的問題轉(zhuǎn)化為了非線性約束優(yōu)化問題。
[Abstract]:The expression of conceptual entities often has some fuzziness, which is contained in semantics. It is of great significance to express fuzzy semantics with appropriate conceptual models. As the smallest representation unit of fuzzy concepts, fuzzy semantic cells play an important role in data mining, machine learning and knowledge discovery. On the concept space (domain) 惟, the fuzzy semantic cell L Pu D, 未 is called the semantic label "on pi", "similar to Pi" and "close to Pi". Where P represents the prototype of the concept I / d is the distance function defined on the domain 惟, and 未 is the probability density function of the distance between other points and Pi defined on [0, 鈭瀅 in the concept space. In the learning of fuzzy semantic cells, we need to pay attention to the three factors of semantic coverage, clarity of description and fuzziness of description, so the learning principle of fuzzy semantic cells is naturally related to the maximum coverage. The most typical and the maximum fuzzy entropy above these three indicators. In this paper, mixed fuzzy semantic cells are based on the learning of fuzzy semantic cells. The ultimate goal of fuzzy semantic cell learning is to find the best L to depict the data set with a certain concept. On the other hand, the mixed fuzzy semantic cells are further extended to consider the set of several related concepts LA= {L1 / L2U. N}, in which each concept corresponds to the data set in which the fuzzy semantic cell Li is used to describe the first concept. The learning of mixed fuzzy semantic cells is to be able to find the most appropriate set of weight parameters W = {W1W2U. W2} to describe the degree of influence or importance of a concept in this set of concepts (themes). Based on the learning principle of fuzzy semantic cells, we need to redefine and calculate the two numerical features of semantic cells: expected granularity R and fuzzy entropy H. Finally, the problem of learning mixed fuzzy semantic cells is transformed into nonlinear constrained optimization problem.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.1

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本文編號(hào)：1818065