机器学习
发布时间：2022-01-17
点击次数：
传统机器学习
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深度学习
强化学习
经典中文文献
卷积神经网络
[1] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251. 卷积神经网络研究综述_周飞燕.pdf
[2] 常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016, 42(9): 1300-1312. 
图像理解中的卷积神经网络_常亮.pdf
特征选择
[1] Chandrashekar G, Sahin F. A survey on feature selection methods[J]. Computers & Electrical Engineering, 2014, 40(1): 16-28. A survey on feature selection methods_2014.pdf
[2] 李郅琴, 杜建强, 聂斌, 等. 特征选择方法综述[J]. 计算机工程与应用, 2019, 55(24): 10-19. 特征选择方法综述_2019.pdf
[3] 姚旭, 王晓丹, 张玉玺, 等. 特征选择方法综述[J]. 控制与决策, 2012, 27(002): 161-166. 特征选择方法综述_2012.pdf
[4] Vergara J R, Estévez P A. A review of feature selection methods based on mutual information[J]. Neural computing and applications, 2014, 24(1): 175-186. A review of feature selection methods based on mutual information_2014.pdf
[5] Miao J, Niu L. A survey on feature selection[J]. Procedia Computer Science, 2016, 91: 919-926. A Survey on Feature Selection_2016.pdf
[6] Xue B, Zhang M, Browne W N, et al. A survey on evolutionary computation approaches to feature selection[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(4): 606-626. A Survey on Feature Selection_2016.pdf
[7] Li J, Cheng K, Wang S, et al. Feature selection: A data perspective[J]. ACM computing surveys (CSUR), 2017, 50(6): 1-45.
Feature Selection A Data Perspective_2017.pdf
[8] Xue B, Zhang M, Browne W N. Particle swarm optimization for feature selection in classification: A multi-objective approach[J]. IEEE Transactions on Cybernetics, 2013, 43(6): 1656-1671. Particle Swarm Optimization for Feature Selection in_2013.pdf
[9] Zhang Y, Gong D, Gao X, et al. Binary differential evolution with self-learning for multi-objective feature selection[J]. Information Sciences, 2020, 507: 67-85. Binary differential evolution with self-learning for_2020.pdf