bayesian classification造句
例句與造句
- Bayesian classification model based on attribute correlation analysis
基于屬性相關(guān)性分析的貝葉斯分類模型 - According to the criteria , the advancement of bayesian classification is evident
綜合這幾個(gè)指標(biāo)，貝葉斯分類算法的優(yōu)點(diǎn)較為突出。 - The bayesian classification and identification method based on normal - inverted wishart prior distribution
先驗(yàn)分布的貝葉斯分類識(shí)別方法研究 - The often - used classification is classification by decision tree induction , bayesian classification and bayesian belief networks , k - nearest neighbor classifiers , rough set theory and fuzzy set approaches
分類算法常見的有判定樹歸納分類、貝葉斯分類和貝葉斯網(wǎng)絡(luò)、 k -最臨近分類、粗糙集方法以及模糊集方法。 - There are many techniques for data classification such as decision tree induction , bayesian classification and bayesian belief networks , association - based classification , genetic algorithms , rough sets , and k - nearest neighbor classifiers
挖掘分類模式的方法有多種，如決策樹方法、貝葉斯網(wǎng)絡(luò)、遺傳算法、基于關(guān)聯(lián)的分類方法、粗糙集和k -最臨近方法等等。 - It's difficult to find bayesian classification in a sentence. 用bayesian classification造句挺難的
- Naive bayesian classification algorithm is not satisfying when deployed to continuous attribute . therefore , the paper proposes a new discretization method under the hint of holte ' s 1r ( one rule ) discretization technique and the mechanism of entropy
樸素貝葉斯分類算法應(yīng)用于連續(xù)屬性值時(shí)并不太理想，為此本文結(jié)合holte的1r離散化方法和熵的原理，提出了一種新的離散化方法。 - Bayesian classification is based on bayesian theorem . it can be comparable in interpretability with decision tree and in speed with neural network classifiers . bayesian classifiers have also exhibited high accuracy and speed when applied to large databases
該算法基于貝葉斯定理，可解釋性方面可以與判定樹相比，準(zhǔn)確度可和神經(jīng)網(wǎng)絡(luò)分類算法相媲美，用于大型數(shù)據(jù)庫時(shí)該算法已表現(xiàn)出高準(zhǔn)確度與高速度。 - Unlike other classifications , bayesian classification bases on mathematics and statistics , and its foundation is bayesian theory , which answers the posterior probability . theoretically speaking , it would be the best solution when its limitation is satisfied
與其它分類方法不同，貝葉斯分類建立在堅(jiān)實(shí)的數(shù)理統(tǒng)計(jì)知識(shí)基礎(chǔ)之上，基于求解后驗(yàn)概率的貝葉斯定理，理論上講它在滿足其限定條件下是最優(yōu)的。 - After dividing proper nouns in two categories , this paper discusses different algorithms for these two categories : for the first category we use proper nouns database to recognize it , and for the second category we use the recognizing method base on native bayesian classification algorithm
然后對(duì)這兩類專有名詞設(shè)計(jì)不同的識(shí)別方法:對(duì)第一類專有名詞使用的基于專有名詞詞庫的識(shí)別算法;對(duì)第二類專有名詞使用的基于樸素貝葉斯分類的識(shí)別算法。 - Monte carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory . when comes to bayesian classification , it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability . then , it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one
蒙特卡羅是一種采用統(tǒng)計(jì)抽樣理論近似求解數(shù)學(xué)或物理問題的方法，它在用于解決貝葉斯分類時(shí)，首先根據(jù)已知的先驗(yàn)概率獲得各個(gè)類標(biāo)號(hào)未知類的條件概率分布，然后利用某種抽樣器，分別得到滿足這些條件分布的隨機(jī)數(shù)據(jù)，最后統(tǒng)計(jì)這些隨機(jī)數(shù)據(jù)，就可以得到各個(gè)類標(biāo)號(hào)未知類的后驗(yàn)概率分布。 - In tcm this pattern is called pair of medicine , and it can be resolved by frequent pattern mining . the symptom complex diagnose can be treated as a bayesian training and a bayesian classification on large clinical database cases . the critical step to resolve the chinese prescription compounding is to build an appropriate model to express the progress of it
中藥知識(shí)發(fā)現(xiàn)集中在發(fā)現(xiàn)常用的單味藥合用模式，在中醫(yī)術(shù)語中稱之為藥對(duì)，這可以用高頻集發(fā)現(xiàn)來解決；中醫(yī)癥候診斷可以看成是在大量臨床案例庫上的貝葉斯訓(xùn)練器和分類器；解決方劑配伍問題的關(guān)鍵是建立起一個(gè)合適的配伍計(jì)算機(jī)模型。 - This paper mainly deals with the multivariate bayesian inference theory used in the modern economical and management science . this includes the bayesian inference theory about three important kinds of linear models , including the single equation model , multiple equation model system and var ( p ) predictive model , and their application in economic forecasting and quality control , and also the design for the bayesian classification identification method among multiple populations
本文主要研究現(xiàn)代經(jīng)濟(jì)管理中的多元貝葉斯推斷理論，包括單方程模型、多方程模型系統(tǒng)和向量自回歸var ( p )模型的貝葉斯推斷理論及其在經(jīng)濟(jì)預(yù)測(cè)與質(zhì)量控制中的應(yīng)用，以及多總體的貝葉斯分類識(shí)別方法的構(gòu)造。 - Pvm , belongs to now , has been used widely . the paper implements the parallel algorithm of optimization bayesian classification on pvm , and analyzed acceleration rate and complexity . the analysis indicates that it is excellence when where is amount of class or the data is very large
本論文在pvm的基礎(chǔ)上研究并實(shí)現(xiàn)了優(yōu)化貝葉斯算法的并行化，并且分析了該算法的加速比和時(shí)間復(fù)雜度，分析表明在類比較多、或者待分類的數(shù)據(jù)樣本比較多時(shí)，用該并行算法可以較大幅度提高數(shù)據(jù)分類的效率。 - Nowadays , many classification methods and some prediction technologies have been put forward , such as classification by decision tree induction , bayesian classification , classification by backpropagation , k - nearest neighbor classifiers , linear and nonlinear regression . however , none of them is better than others in all application
在這一研究方向，目前已提出了多種分類方法（如決策樹歸納分類、貝葉斯分類、神經(jīng)網(wǎng)絡(luò)分類和k -最鄰近分類等）和一些預(yù)測(cè)技術(shù)（如線性回歸、非線性回歸等） 。