摘要
为了提高预测准确性,构造了一类优化多示例神经网络参数的改进遗传算法,借助基于反向传播训练的局部搜索算子、排挤操作和适应性操作概率计算方式来提高收敛速度和防止早熟收敛。通过公认的数据集上实验结果的分析和对比,证实了这个改进的遗传算法能够明显地提高多示例神经网络的预测准确性,同时还具有比其他算法更快的收敛速度。
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.
出处
《计算机应用》
CSCD
北大核心
2005年第10期2387-2389,2412,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(6023403060404021)
关键词
多示例神经网络
多示例学习
遗传算法
multi-instance neural networks
multi-instance learning
genetic algorithms
