bayesian classification in Chinese
Translation
Mobile
- 貝葉斯分類器
- "classification" in Chinese : n. 1.選別；分等，分級(jí)；分選。 2.【動(dòng)、植】分類( ...
- "bayesian" in Chinese : 貝葉斯的; 貝葉斯定理的
- "bayes bayesian" in Chinese : 貝葉斯定理的
- "bayesian algorithm" in Chinese : 貝葉斯算法
- "bayesian analysis" in Chinese : 貝葉斯分析
- "bayesian approach" in Chinese : bayes氏方法; 貝斯分析法
- "bayesian classifier" in Chinese : 貝葉斯分類器
- "bayesian estimate" in Chinese : 貝葉斯估計(jì)
- "bayesian estimation" in Chinese : 貝葉斯估計(jì)
- "bayesian formula" in Chinese : 式方程式
- "bayesian inference" in Chinese : 貝葉斯定理; 貝葉斯推斷; 貝葉斯推理; 貝葉斯推論
- "bayesian learning" in Chinese : 貝式學(xué)習(xí)
- "bayesian logic" in Chinese : 貝葉斯邏輯
- "bayesian models" in Chinese : 貝式模型法
- "bayesian network" in Chinese : 貝式網(wǎng)路; 貝式網(wǎng)絡(luò); 貝葉斯網(wǎng)絡(luò)
Examples
- 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。 - 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(kù)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(kù)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(wèn)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。
- More examples： 1 2
Related
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