setting n. 1.安装;装配;装置;安放。 2.(机器的)底座。 3.调整。 4.整齿。 5.锉锯子。 6.配乐;谱曲。 7.(果树的)坐果。 8.【印刷】排字。 9.镶嵌;镶嵌物;镶嵌(宝石等)的框子。 10.【剧,影】剧景;布景;舞台面。 11.背景;(花园的)布置;环境。 12.(天体的)没落;(日、月的)沉落。 13.(潮水、风等的)方向。 14.凝结;凝固;硬化。 15.炮床。 16.一套餐具。 17.【航空】定位;【建筑】下沉。 a setting hen 伏窝的母鸡。 a circle setting 【测】度盘位置。 a setting of butterflies 一组蝴蝶标本。 with a sea setting 用海作背景。 a setting chamber 沉淀室。 a setting tank 澄水池。
The optimized feature set feeds a 3 - class classification module , which is based on the traditional binary svm classifier . and the proposed linear programming svm reduces the burden of the svm classifier and improves its learning speed and classification accuracy . a new algorithm that combined svm with k nearest neighbor ( knn ) is presented and it comes into being a new classifier , which can not only improve the accuracy compared to sole svm , but also better solve the problem of selecting the parameter of kernel function for svm 在研究了数据挖掘、支持向量机及其有关技术的基础上,建立了实现三类水中目标识别的svm方法;采用线性规划svm解决了传统二次规划svm在海量样本情况下导致的时间和空间复杂度问题;提出了将最近邻分类与支持向量机分类相结合的svm - knn分类器应用于水中目标识别的思想,较好地解决了应用支持向量机分类时核函数参数的选择问题,取得了更高的分类准确率。
In further research , the following issues must be considered : 1 ) the standardize of corpus ; 2 ) improve the accuracy of chinese words divided syncopation system , handle the different meanings of one word and recognize the words that do not appear in the dictionary ; 3 ) process semantic analysis ; 4 ) dynamically update the training sets fed back by the user ; 5 ) quantitatively analyze the system performance influenced by different factors , use an appropriate model to compare and evaluate the web text classification system ; 6 ) natural language process ; 7 ) distinguish the disguise of sensitive words 在以后的工作中考虑如下问题: 1 )数据集的标准化; 2 )分词系统精度的提高,对歧义处理以及未登录词识别的能力的提高: 3 )进行合理的语义分析: 4 )利用用户反馈信息动态更新训练集; 5 )定t分析分类器不同要素对分类系统性能的影响,使用合适的模型来比较和评价分类系统; 6 )自然语言理解问题,如“引用”问题; 7 )对于敏感词汇伪装的识别问题。