本文選題：證據(jù)理論 + logistic回歸�。� 參考：《江西師范大學(xué)》2017年碩士論文

【摘要】：證據(jù)理論的最大優(yōu)點(diǎn)在于它能在不知道先驗(yàn)概率的前提下表達(dá)“不確定性”和“不知道”的問題,為不確定性推理提供了可靠的方法。當(dāng)前,在人工智能領(lǐng)域已廣泛應(yīng)用。然而,目前還沒有從數(shù)據(jù)源中獲取證據(jù)函數(shù)的完整方法,也就是說,基本信度分配函數(shù)的構(gòu)造和復(fù)雜證據(jù)網(wǎng)絡(luò)推理方法仍存在許多問題有待研究。本文的所做工作如下:一、針對證據(jù)理論中構(gòu)造基本信度分配函數(shù)(BBA)困難的問題,本文找到了一種加權(quán)基本信度指派函數(shù)的構(gòu)造新方法,并應(yīng)用在多特征圖像分類上。該方法以多類logistic回歸輸出的后驗(yàn)概率與識別正確率構(gòu)造證據(jù)權(quán)重系數(shù),進(jìn)而構(gòu)造出權(quán)重基本信度指派;最后通過加權(quán)D-S證據(jù)融合最終判別類別。實(shí)驗(yàn)結(jié)果表明,該方法能夠克服單一特征分類精度的不穩(wěn)定性,提高分類精度。二、本文把團(tuán)樹傳播算法應(yīng)用在證據(jù)網(wǎng)絡(luò)中,解決了復(fù)雜的多連通知識網(wǎng)絡(luò)結(jié)構(gòu)下的信度推理問題。該方法首先把復(fù)雜多連通網(wǎng)絡(luò)構(gòu)造成一棵團(tuán)樹,并將聯(lián)合信度作為團(tuán)節(jié)點(diǎn)的參數(shù)實(shí)現(xiàn)了復(fù)雜多連通網(wǎng)絡(luò)結(jié)構(gòu)上的證據(jù)網(wǎng)絡(luò)信度推理;在進(jìn)行聯(lián)合信度函數(shù)信息融合過程中,通過引入兩種新的交并運(yùn)算實(shí)現(xiàn)了對DSmT組合規(guī)則的改進(jìn),減少了不確定性。最后,通過一個例子來證明該方法的可行性。
[Abstract]:The greatest advantage of evidence theory lies in its ability to express the problems of "uncertainty" and "not knowing" without knowing the prior probability, which provides a reliable method for uncertain reasoning. At present, it has been widely used in the field of artificial intelligence. However, there is no complete method to obtain the evidence function from the data source, that is, there are still many problems to be studied in the construction of the basic reliability assignment function and the reasoning method of the complex evidence network. The work of this paper is as follows: firstly, in view of the difficulty of constructing the basic reliability assignment function (BBA) in evidence theory, a new method of constructing weighted basic reliability assignment function is found and applied to the classification of multi-feature images. In this method, the weight coefficient of evidence is constructed by using the posterior probability and recognition accuracy of multi-class logistic regression output, and then the basic reliability assignment of weight is constructed. Finally, the final discriminant category is obtained by weighted D-S evidence fusion. The experimental results show that this method can overcome the instability of the classification accuracy of a single feature and improve the classification accuracy. Secondly, the cluster tree propagation algorithm is applied to the evidence network to solve the reliability reasoning problem under the complex multi-connected knowledge network structure. Firstly, the complex multi-connected network is constructed into a cluster tree, and the joint reliability is taken as the parameter of the cluster node to realize the reliability reasoning of the evidential network on the complex multi-connected network structure, and in the process of information fusion of the joint reliability function, Two new intersection and union operations are introduced to improve the DSmT combination rules and reduce the uncertainty. Finally, an example is given to prove the feasibility of the method.
【學(xué)位授予單位】：江西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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本文編號：1977934