摘要
音频例子识别与检索的主要任务是构造一个良好的分类学习机 ,而在构造过程中 ,从含有冗余样本的训练库中选择最佳训练例子、节省学习机的训练时间是构造分类机面临的一个挑战 ,尤其是对含有大样本训练库音频例子的识别 由于支持向量是支持向量机中的关键例子 ,提出了增量学习支持向量机训练算法 在这个算法中 ,训练样本被分成训练子库按批次进行训练 ,每次训练中 ,只保留支持向量 ,去除非支持向量 与普通和减量支持向量机对比的实验表明 ,算法在显著减少训练时间前提下 。
The primary task of audio clip recognition and retrieval is to construct a well performance classifier learning machine How to choose informative training instance from redundant training database and reduce training time of classifier machine is a challenge during the construction of classifier machine, especially for audio clip recognition with large size training database Since support vector is the key instance in support vector machine (SVM), an algorithm to train SVM with incremental learning is proposed In this algorithm, training database is segmented into sub databases and each sub database is trained in batch During each training process, only support vector is reserved for future training and non support vector is discarded Compared with traditional and decremental SVMs, this training algorithm obviously reduces training time and obtains high correct rates of recognition and retrieval
出处
《计算机研究与发展》
EI
CSCD
北大核心
2003年第7期950-955,共6页
Journal of Computer Research and Development
基金
国家自然科学基金 ( 60 2 72 0 3 1)
浙江省自然科学基金 (ZD0 2 12 )
教育部博士点基金 ( 2 0 0 10 3 3 5 0 49)
国家"十五"重大科技攻关项目 ( 2 0 0 1BA10 1A0 7 0 3 )
浙江省科技计划重点科研项目 ( 2 0 0 3C2 10 10 )
关键词
增量学习
支持向量机
音频例子识别检索
分类学习机
incremental learning
support vector machine
audio clip recognition and retrieval
