本文關(guān)鍵詞：基于改進(jìn)SVM的創(chuàng)新項(xiàng)目評(píng)價(jià)模型研究　出處：《重慶理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 創(chuàng)新 降維 項(xiàng)目評(píng)價(jià) 遺傳算法 支持向量機(jī)

【摘要】：科技創(chuàng)新是促進(jìn)國家經(jīng)濟(jì)增長的重要源泉,也是我國創(chuàng)新體系中的重要組成部分。目前全國各地均已建立了不同類型的項(xiàng)目創(chuàng)新平臺(tái),為研究者、生產(chǎn)者以及管理者等參與主體開展深度合作、資源共享、進(jìn)行合力創(chuàng)新攻關(guān)帶來了方便。對(duì)項(xiàng)目創(chuàng)新平臺(tái)上的項(xiàng)目數(shù)據(jù)進(jìn)行提取分析,進(jìn)行項(xiàng)目的準(zhǔn)確評(píng)價(jià)有著重要意義。由于創(chuàng)新過程是一個(gè)極其復(fù)雜和不確定性的動(dòng)態(tài)社會(huì)過程,整個(gè)項(xiàng)目中涉及到的數(shù)據(jù)非常復(fù)雜尤其是今后隨著各領(lǐng)域的交叉合作,平臺(tái)上的項(xiàng)目數(shù)據(jù)會(huì)越來越多,如何對(duì)項(xiàng)目進(jìn)行客觀評(píng)價(jià),為管理者提供準(zhǔn)確的決策信息成為了一個(gè)亟需解決的重要問題。為解決以上問題,論文從數(shù)據(jù)挖掘的角度出發(fā),結(jié)合機(jī)器學(xué)習(xí)方法建立評(píng)價(jià)模型。論文所涉及的主要工作包括以下幾個(gè)方面:第一,介紹了關(guān)于科技創(chuàng)新的背景和意義,概述了當(dāng)前國內(nèi)外學(xué)者對(duì)解決創(chuàng)新項(xiàng)目評(píng)價(jià)問題的研究現(xiàn)狀,以及解決問題所使用到的相關(guān)理論知識(shí),并分析了其不足之處,最后展望了建立模型所使用的新方法。第二,論述了建立項(xiàng)目評(píng)價(jià)模型所涉及到的理論方法和技術(shù),主要包括降維算法理論、遺傳算法、支持向量機(jī)理論等知識(shí)。第三,依據(jù)確立的項(xiàng)目評(píng)價(jià)指標(biāo)體系采集相應(yīng)的數(shù)據(jù)并預(yù)處理獲得實(shí)驗(yàn)數(shù)據(jù),選用不同的核函數(shù)利用支持向量機(jī)分類器進(jìn)行學(xué)習(xí)訓(xùn)練,通過比較預(yù)測準(zhǔn)確率得出了最佳的核函數(shù)。為了削減數(shù)據(jù)的冗余信息,減少分類器的學(xué)習(xí)訓(xùn)練時(shí)間提升模型的性能,使用不同的降維算法對(duì)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行特征提取,將處理后的樣本輸入分類器進(jìn)行分類任務(wù),實(shí)驗(yàn)結(jié)果表明LLE算法在分類中應(yīng)用良好。第四,通過對(duì)構(gòu)建的項(xiàng)目評(píng)價(jià)模型LLE+SVM進(jìn)行分析,指出了不足之處并進(jìn)行了兩點(diǎn)相關(guān)改進(jìn)。主要是從前端降維和后端分類入手,利用樣本數(shù)據(jù)自身帶有的類別標(biāo)簽信息將傳統(tǒng)的局部線性嵌入算法改進(jìn)為帶有監(jiān)督功能的降維方法,增強(qiáng)了對(duì)數(shù)據(jù)的降維效果;針對(duì)支持向量機(jī)中核函數(shù)參數(shù)以及懲罰因子最優(yōu)問題,使用改進(jìn)遺傳算法對(duì)支持向量機(jī)進(jìn)行參數(shù)尋優(yōu),得到整體性能最佳的支持向量機(jī)。通過上述改進(jìn)最終確立了高效的創(chuàng)新項(xiàng)目評(píng)價(jià)模型。準(zhǔn)確評(píng)價(jià)是對(duì)創(chuàng)新項(xiàng)目進(jìn)行有效管理的首要條件,基于本文所構(gòu)建的項(xiàng)目評(píng)價(jià)模型能夠高效準(zhǔn)確地評(píng)估項(xiàng)目,對(duì)于以后在創(chuàng)新項(xiàng)目平臺(tái)上進(jìn)行快速客觀地管理決策項(xiàng)目,具有重要的現(xiàn)實(shí)意義。
[Abstract]:Scientific and technological innovation is an important source of promoting national economic growth and an important part of China's innovation system. At present, different types of project innovation platforms have been established all over the country for researchers. Producers as well as managers and other participants to carry out in-depth cooperation, resource sharing, joint efforts to solve the key innovation brought convenience. The project data on the project innovation platform for extraction and analysis. It is very important to evaluate the project accurately because the innovation process is a very complex and uncertain dynamic social process. The data involved in the whole project is very complex, especially with the cross-cooperation of various fields in the future, the project data on the platform will be more and more, how to evaluate the project objectively. Providing accurate decision information for managers has become an important problem that needs to be solved. In order to solve the above problems, this paper starts from the angle of data mining. The main work of this paper includes the following aspects: first, the background and significance of scientific and technological innovation are introduced. This paper summarizes the current research status of domestic and foreign scholars to solve the problem of evaluation of innovative projects, as well as the relevant theoretical knowledge used to solve the problem, and analyzes its shortcomings. Finally, the new methods used to build the model are prospected. Secondly, the theoretical methods and techniques involved in the establishment of the project evaluation model are discussed, including dimensionality reduction algorithm theory and genetic algorithm. Support vector machine theory and other knowledge. Third, according to the established project evaluation index system to collect the corresponding data and pre-processing to obtain experimental data, select different kernel functions to use support vector machine classifier for learning and training. The best kernel function is obtained by comparing the prediction accuracy. In order to reduce the redundant information of the data, the learning and training time of the classifier is reduced to improve the performance of the model. Different dimensionality reduction algorithms are used to extract the features of experimental data and the processed samples are input into the classifier for classification task. The experimental results show that the LLE algorithm is well applied in the classification. 4th. Through the analysis of the project evaluation model LLE SVM, this paper points out the shortcomings and makes two related improvements, mainly starting with the front-end reduction and back-end classification. The traditional local linear embedding algorithm is improved to dimensionality reduction method with supervisory function by using the class label information of the sample data itself, which enhances the dimension reduction effect of the data. Aiming at the optimization of kernel function parameters and penalty factors in support vector machines, an improved genetic algorithm is used to optimize the parameters of support vector machines. Get the best overall performance of the support vector machine. Through the above improvements finally established an efficient innovation project evaluation model. Accurate evaluation is the most important condition for the effective management of innovation projects. The project evaluation model constructed in this paper can evaluate the project efficiently and accurately. It is of great practical significance to manage the project quickly and objectively on the platform of innovative project.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP18;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張寧;萬忠梅;;基于層次分析法的梅河口市地質(zhì)災(zāi)害易發(fā)性區(qū)劃[J];山東農(nóng)業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年02期

2 徐剛;姜強(qiáng)男;周艷;;產(chǎn)學(xué)研協(xié)同創(chuàng)新主體客觀效用評(píng)價(jià)研究[J];重慶理工大學(xué)學(xué)報(bào)(社會(huì)科學(xué));2015年11期

3 熊瑤;謝金亮;鄒俐愛;;數(shù)據(jù)包絡(luò)分析法在醫(yī)院運(yùn)營效率評(píng)價(jià)中的應(yīng)用[J];醫(yī)學(xué)與社會(huì);2015年11期

4 李林;賈佳儀;章宜;;區(qū)域產(chǎn)業(yè)協(xié)同創(chuàng)新項(xiàng)目的選擇評(píng)價(jià)研究[J];中國科技論壇;2015年08期

5 匡小平;鄧威;李文祿;;基于灰色層次分析法的戰(zhàn)儲(chǔ)物資管理效能評(píng)價(jià)[J];軍事交通學(xué)院學(xué)報(bào);2014年11期

6 李志義;沈之銳;義梅練;;貝葉斯分類算法在社交網(wǎng)站信息過濾中的應(yīng)用分析[J];圖書情報(bào)工作;2014年13期

7 饒萍;王建力;王勇;;基于多特征決策樹的建設(shè)用地信息提取[J];農(nóng)業(yè)工程學(xué)報(bào);2014年12期

8 姜雪;盧文喜;楊青春;趙海卿;;應(yīng)用支持向量機(jī)評(píng)價(jià)土壤環(huán)境質(zhì)量[J];中國環(huán)境科學(xué);2014年05期

9 謝思全;鹿媛媛;李葉妍;;科技協(xié)同創(chuàng)新績效評(píng)價(jià)指標(biāo)體系初探[J];現(xiàn)代管理科學(xué);2014年01期

10 李曉龍;張兆翔;王蘊(yùn)紅;劉慶杰;;深度學(xué)習(xí)在航拍場景分類中的應(yīng)用[J];計(jì)算機(jī)科學(xué)與探索;2014年03期

相關(guān)博士學(xué)位論文 前1條

1 王靖;流形學(xué)習(xí)的理論與方法研究[D];浙江大學(xué);2006年

，

本文編號(hào)：1397778