%0 Journal Article
%T Optimizing multi-instance neural networks based on an improved genetic algorithm<br>基于改进遗传算法的多示例神经网络优化
%A CAI Zi-xing
%A SUN Guo-rong
%A LI Mei-yi
%A <br>蔡自兴
%A 孙国荣
%A 李枚毅
%J 计算机应用
%D 2005
%I 
%X In order to achieve higher predictive accuracy, an improved genetic algorithm for optimizing multi-instance neural networks was presented. Convergence rate was increased and premature convergence was overcome by means of local search operator, suppress operator and adaptive calculations of probabilities for operators. Some experiments on well-known test data show that multi-instance neural networks that are optimized by the improved genetic algorithm heighten significantly predictive accuracy and computational expensiveness of the algorithm is less than other algorithms.
%K multi-instance neural networks
%K multi-instance learning
%K genetic algorithms<br>多示例神经网络
%K 多示例学习
%K 遗传算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=8840E81794F3E6D6&yid=2DD7160C83D0ACED&vid=C5154311167311FE&iid=F3090AE9B60B7ED1&sid=44DA216FA1E0217E&eid=CCBC80F4027FC41E&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=7