樸素模型 的英文怎麼說
中文拼音 [sùmóxíng]
樸素模型
英文
naive model-
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。Mixed naive bayes classifier model
混合式樸素貝葉斯分類模型The key of model learning of semi - naive bayesian classifier is how to combine feature attributes effectively
目前半樸素貝葉斯分類模型學習的關鍵是如何有效組合特徵屬性。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。Semi - naive bayesian classifier extends the structure of naive bayesian classifier in order to get rid of the limit of the assumption of independence between feature attributes of naive bayesian classifier and improve the performance of classification
半樸素貝葉斯分類模型對樸素貝葉斯分類模型的結構進行了擴展，其目的是為了突破樸素貝葉斯分類模型特徵屬性間獨立性假設限制，提高分類性能。The thesis analyses and parses openoffice. org document from text classification viewpoint, depicts the methods of extracting content, formatting, structure and descriptive information, which are most related to classification, from openoffice. org documents, and then constructs three different classification models for openoffice. org documents, respectively called label components classifier, structure components classifier and comprehensive classifier. the thesis implements these three classifiers through na ? ve bayes
本文從面向分類的角度深入地分析了openoffice . org文檔，描述了從文檔中抽取與分類最相關的內容和格式、結構以及文檔描述信息的方法，構建了標簽組件法、結構組件法和綜合法三種不同的文本分類模型，最後用樸素貝葉斯方法實現了openoffcice . org文檔的三種分類器。We explored the specific machine learning technique, i. e. bayesian technique in this paper, in the hope of automatically creating such kind of metadata from training data. we assigned wsdl documents to different categories using bayesian latent semantic model. with the frame of blsm, our system classified wsdl documents only by a few of latent class variables and no labeled data
本論文中基於貝葉斯技術的web服務分類演算法的主要思想是通過引入貝葉斯潛在語義模型，首先將含有潛在類別主題變量的wsdl文檔分配到相應的類主題中；接著利用樸素貝葉斯模型，結合前一階段的知識，完成對未含類主題變量的文檔作標注。分享友人