貝葉斯機器 的英文怎麼說

中文拼音 [bèiyèsījīqì]
貝葉斯機器 英文
bayes machine
  • 貝 : 名詞1 [動物學] （蛤螺等有殼軟體動物的統稱） cowry; cowrie; shellfis 2 （古代用貝殼做的貨幣） cowr...
  • 斯 : Ⅰ名詞（古代驅疫時用的面具） an ancient maskⅡ形容詞[書面語] （醜陋） ugly
  • 機 : machineengine
  • 器 : 名詞1. （器具） implement; utensil; ware 2. （器官） organ 3. （度量; 才能） capacity; talent 4. （姓氏） a surname
  • 貝葉 : （印度貝多羅 pattra 樹的葉子, 古代印度人用以寫佛經） pattra leaves
  • 機器 : 1. （用來轉換或利用機械能的機構） machine; machinery; engine 2. （引申為機構） apparatus; organ
  1. Author develop three text dassiliers lilie naive - bayes classifier, k nearest neighbor classifier and svm classifier. furthermore, including the three classifiers, one text categorisation system is built up, and it has high prachcability

    作者採用三個模型，實現了樸素貝葉斯分類器、 k近鄰分類器和支持向量機分類器三個中文文本分類器，集成了一個實用性較強的實驗系統。
  2. 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

    蒙特卡羅是一種採用統計抽樣理論近似求解數學或物理問題的方法，它在用於解決貝葉斯分類時，首先根據已知的先驗概率獲得各個類標號未知類的條件概率分佈，然後利用某種抽樣器，分別得到滿足這些條件分佈的隨機數據，最後統計這些隨機數據，就可以得到各個類標號未知類的后驗概率分佈。
  3. 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

    中藥知識發現集中在發現常用的單味藥合用模式，在中醫術語中稱之為藥對，這可以用高頻集發現來解決；中醫癥候診斷可以看成是在大量臨床案例庫上的貝葉斯訓練器和分類器；解決方劑配伍問題的關鍵是建立起一個合適的配伍計算機模型。
  4. Comparing with non - bnyain methods, it ' s prominent featares lay in that it combines the prior and posterior information, which avoids the disadvantag of subjective bias caused by simply using the prior information only, of blind search caused by the incomplete sample information, of noise affection caused by simply using the sample information only if we choice a suitable priof, we can conduct the bayesian leaming effectively, so it fits the problems of data mining and machine leaming that possess charaters of probability and statistics, especially when the samples are rare

    與非貝葉揚方法相比，貝葉斯方法的特出特點是其學習機制可以綜合先驗信息和后驗信息，既可避免只使用先驗信息可能帶來的主觀偏見，和缺乏樣本信息時的大量盲目搜索與計算，也可避免只使用樣本信息帶來的噪音的影響只要合理地確定先驗，就可以進行有效的學習。因此，適用於具有概率統計特徵的數據採掘和機器學習（或發現）問題，尤其是樣本難得的問題
  5. Because of ineffectiveness of naive bayes model for text classification, this thesis proposed integrating boosting theory of machine learning in classification process, boost naive bayes categorization model through many times training. improved by experiments, mutual information and naive bayes integrated with boosting bring very good precision, recall, and f1 score

    鑒于樸素貝葉斯的分類效果不佳，本論文又提出將機器學習中的boosting思想結合到樸素貝葉斯的分類模型中，對樸素貝葉斯模型進行提升，實驗證明，改進的互信息和給合了boosting思想的樸素貝葉斯分類模型均產生良好的分類效果?分準率、分全率及f1值。
  6. Classification has always been a central issue on data mining, machine learning and pattern recognition, classifier, as an important model and method of machine learning and data mining, is very important to the development and application of machine learning and the data mining. the classifier ’ s effect closely correlates with the characteristic of data sets, at present, the construction of classifier is generally based on the character of different datasets, there is no such a classifier which is suitable for any data sets. under uncertain conditions, the bayes network is a powerful tools for the knowledge expression and inference, but for difficulties in constructing its network structure and very high time complexity, it has not been considered as a classifier algorithm until the emergence of na ? ve - bayes classifier

    分類一直是數據挖掘、機器學習和模式識別等研究的核心問題，貝葉斯網路是作為知識表示和推理的強大工具，由於搜索空間巨大和學習困難的原因，直到樸素貝葉斯理論的出現才被作為分類器演算法，改進樸素貝葉斯分類器是貝葉斯分類器學習的一個主要的研究方向。遺傳演算法本質上是一種求解問題的高效并行全局搜索演算法，適合應用於那些改進的分類器的結構學習中。本文提出了一種基於遺傳演算法的ban分類器演算法。
  7. We try the unsupervised wsd method based on equivalent pseudowords by the na ? ve bayes model and maximum entropy in paper. it gets 81 % correct rate on the test data of senseval - 3, which is obvious better than supervised method accordingly

    利用得到的兩種較優的機器學習方法:貝葉斯模型及最大熵模型，本文嘗試了基於等價偽詞的無指導詞義消歧方法，在senseval - 3的測試數據上獲得了81 %的正確率，明顯優于相應的有指導方法。
  8. This paper presents a method of automatic text categorization for chinese web pages, which mainly includes such models as chinese web pages acquirement, chinese word splitter, feature selection and native bayes machine learning and classification

    摘要提出了一種中文網頁自動分類的方法，主要包括中文網頁的自動抓取、中文分詞、特徵選取、貝葉斯機器學習與分類等功能模塊。
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