本文選題：隨機賦權(quán)網(wǎng)絡 + 前饋神經(jīng)網(wǎng)絡��； 參考：《河北大學》2017年碩士論文

【摘要】：隨著大數(shù)據(jù)時代的來臨,數(shù)據(jù)的規(guī)模越來越大,同時數(shù)據(jù)類型也呈現(xiàn)出多樣性。數(shù)據(jù)有數(shù)值型的,也有符號型數(shù)據(jù)及符號型和數(shù)值型的混合型數(shù)據(jù)。如何從各種類型的海量數(shù)據(jù)中快速準確地挖掘出有價值的知識,也包括從符號值數(shù)據(jù)中挖掘有價值的知識,已成為機器學習領(lǐng)域的研究熱點,具有重要的應用價值。分類問題是機器學習研究的主要問題之一,本文的主要工作是研究基于隨機賦權(quán)網(wǎng)絡的符號值數(shù)據(jù)分類。隨機賦權(quán)神經(jīng)網(wǎng)絡也稱為極速學習機(Extreme Learning Machine,ELM),其主要思想是通過隨機化方法提高學習速度。本文研究了符號值隨機賦權(quán)神經(jīng)網(wǎng)絡,并與C4.5算法從三個方面進行了實驗比較:(1)時間復雜度與泛化能力;(2)訓練樣例大小對算法性能影響;(3)處理不完整數(shù)據(jù)的能力。得出了如下有價值的結(jié)論:(1)ELM和C4.5在測試精度上,沒有本質(zhì)的差別,但是ELM具有更快的學習速度;(2)測試精度并不總是隨著樣例數(shù)的增加而增加;(3)與C4.5相比,ELM具有更強的抗噪能力。
[Abstract]:With the advent of big data era, the scale of data becomes larger and larger, and the data types also present diversity. There are numerical, symbolic and mixed data. How to quickly and accurately mine valuable knowledge from all kinds of massive data, including symbolic value data, has become a hot topic in the field of machine learning and has important application value. Classification problem is one of the main problems in machine learning. The main work of this paper is to study the classification of symbolic value data based on stochastic weight network. Stochastic weighted neural network (RWNN) is also called extreme Learning Machine (ELMN). Its main idea is to improve the learning speed by means of randomization. In this paper, the symbolic value random weighted neural network is studied and compared with C4.5 algorithm from three aspects: 1) time complexity and generalization ability / 2) the ability of training sample size to affect the performance of the algorithm is compared with that of C4.5 algorithm in processing incomplete data. The results show that there is no essential difference between ELM and C4.5 in testing accuracy, but ELM has a faster learning speed. The test accuracy does not always increase with the increase of sample number. Compared with C4.5, ELM has stronger anti-noise ability.
【學位授予單位】：河北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP181

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本文編號：1954390