欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
自从Kivinen和Warmuth提出权衡正确性与保守性的在线学习框架后,此在线学习框架已被广泛引用.但是在Kivinen和Warmuth提出的梯度下降和指数梯度下降算法中,对目标函数中的损失函数求导过程中使用近似步骤会引起在线学习结果恶化.文中,运用对偶最优化理论,提出了非近似的基于平方距离相关熵损失函数分类算法和基于相关熵距离相关熵损失函数分类算法,通过4种不同维数的真实数据集的实验研究,验证了提出算法的分类预测性能.
英文摘要：
Since the online learning framework that make a compromise of the correctness and conservativeness is proposed by Kivinen and Warmuth, the framework have been referenced widely, but in gradient descent and exponentiated gradient algorithms proposed by Kivinen and Warmuth, the approximation step in the derivation of loss function of objection function lead to bad results. In this work, by means of duality theory of optimization, the novel non-approxima- tion classifier algorithms based on square distance and relative entropy loss, relative entropy dis- tance and relative entropy loss are proposed. Experimental results show that the proposed classi- fiers are always more accurate than the gradient descent and exponentiated gradient algorithms proposed by Kivinen and Warmuth in the real datasets.
同期刊论文项目
同项目期刊论文
A control structure selection method based on multivariable generalized predictive control for unsta
期刊信息
位置：