山东大学学报(工学版)
山東大學學報(工學版)
산동대학학보(공학판)
JOURNAL OF SHANDONG UNIVERSITY(ENGINEERING SCIENCE)
2015年
1期
13-18
,共6页
浩庆波%牟少敏%尹传环%昌腾腾%崔文斌
浩慶波%牟少敏%尹傳環%昌騰騰%崔文斌
호경파%모소민%윤전배%창등등%최문빈
局部支持向量机%k最近邻%k均值聚类%核函数%分类%纹理特征
跼部支持嚮量機%k最近鄰%k均值聚類%覈函數%分類%紋理特徵
국부지지향량궤%k최근린%k균치취류%핵함수%분류%문리특정
local support vector machine%k-nearest neighbor%k-means clustering%kernel function%classification%texture features
为进一步改善局部支持向量机的分类效率和分类精度,提出一种改进的局部支持向量机算法。该算法对每类训练样本分别进行聚类,使用聚类生成的样本中心点集代替样本,使用改进的k最近邻算法选取测试样本的k个近邻。分别在UCI数据集和自建树皮图像数据集上对本研究算法的有效性进行测试。实验结果表明,本研究提出的算法在分类精度和效率上具有一定的优势。
為進一步改善跼部支持嚮量機的分類效率和分類精度,提齣一種改進的跼部支持嚮量機算法。該算法對每類訓練樣本分彆進行聚類,使用聚類生成的樣本中心點集代替樣本,使用改進的k最近鄰算法選取測試樣本的k箇近鄰。分彆在UCI數據集和自建樹皮圖像數據集上對本研究算法的有效性進行測試。實驗結果錶明,本研究提齣的算法在分類精度和效率上具有一定的優勢。
위진일보개선국부지지향량궤적분류효솔화분류정도,제출일충개진적국부지지향량궤산법。해산법대매류훈련양본분별진행취류,사용취류생성적양본중심점집대체양본,사용개진적k최근린산법선취측시양본적k개근린。분별재UCI수거집화자건수피도상수거집상대본연구산법적유효성진행측시。실험결과표명,본연구제출적산법재분류정도화효솔상구유일정적우세。
In order to further improve the classification efficiency and precision of local support vector machine,a new al-gorithm was proposed.The two major improvements were as follows.First,every type of training samples was clustered seperately,and the training samples were substituted for sample centers generated by clustering.Second,the k nearest neighbors of test samples were selected by using the improved k-nearest neighbor algorithm.Tests were done on UCI data sets and bark image data sets made by the proposed algorithm to verify its effectiveness.Experimental results demonstrated that this algorithm had certain superiority of classification accuracy and efficiency.