图像分析与机器视觉课程作业，是一篇关于行人重识别的论文《Joint Discriminative and Generative Learning for Person Re-identification》的解读报告。

PCA、LDA、SVM、K-means等各种实验报告及数据及代码及论文，你想要的这儿都有

我读过的ML论文摘要记录在Issues中 阅读论文时要问自己的问题。（参考： ） 1. What previous research and ideas were cited that this paper is building off of? (this info tends to live in the introduction) 2. Was there reasoning for performing this research, if so what was it? (introduction section) 3. Clearly list out the objectives of the study 4. Was any equipment/software used? (methods section) 5. What variables were measured

机器学习老师推荐的论文，是关于民航的。基于使用现有的支持向量机解决机场航班延误预警问题存在未充分利用先验知识和 训练需花费大量时间和空间的问题,提出了基于中心约束最小闭包球的加权多类算法。该算 法首先利用先验知识确定一种新的基于相对紧密度的方法计算样本权值并将其融合到支持向 量机中,然后转化为中心约束的最小闭包球进行训练。实验结果表明,该方法比现有的支持向 量机具有更合理的分类面并且训练速度得到大大提高。

25篇机器学习经典论文合集，有需要欢迎积分自取 Efficient sparse coding algorithms论文附有代码 [1] Zheng S, Kwok J T. Follow the moving leader in deep learning[C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 4110-4119. [2] Kalai A, Vempala S. Efficient algorithms for online decision problems[J]. Journal of Computer and System Sciences, 2005, 71(3): 291-307. [3] Kingma, D. and Ba, J. Adam: A method for stochastic optimization. In Proceedings of the International Conference for Learning Representations, 2015. [4] Lee H, Battle A, Raina R, et al. Efficient sparse coding algorithms[C]//Advances in neural information processing systems. 2007: 801-808. [5] Fan J, Ding L, Chen Y, et al. Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery[J]. 2019. [6] Z. Lai, Y. Chen, J. Wu, W. W. Keung, and F. Shen, “Jointly sparse hashing for image retrieval,” IEEE Transactions on Image Processing, vol. 27, no. 12, pp. 6147–6158, 2018. [7] Z. Zhang, Y. Chen, and V. Saligrama, “Efficient training of very deep neural networks for supervised hashing,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 2016, pp. 1487–1495. [8] Wei-Shi Zheng, Shaogang Gong, Tao Xiang. Person re-identification by probabilistic relative distance comparison[C]// CVPR 2011. IEEE, 2011. [9] Liao S, Hu Y, Zhu X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 2197-2206. [10] Liu X, Li H, Shao J, et al. Show, tell and discriminate: Image captioning by self-retrieval with partially labeled data[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 338-354. [11] Yao T, Pan Y, Li Y, et al. Exploring visual relationship for image captioning[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 684-699. [12] Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang., ”Image

很好的专业论文，推荐有志于做车辆识别的同学阅读。 很好的专业论文，推荐有志于做车辆识别的同学阅读。

虽然 2019 年已经渐行渐远，但回顾这一年，人工智能领域发生的很多大事件产生的影响依然深远。在 AI 引发颇多思考的一年中，优秀的论文也源源不断地诞生，让我们得以了解和学习，在 AI 领域各个方向的技术所取得的新进展。我们收集了 Reddit 上，网友分享的「2019 最喜欢的论文」，与大家分享，希望大家能从这些论文中，对 2020 年人工智能的发展方向、前沿技术，略窥一二。

包含十几篇机器学习数据挖掘论文 对大家相信有非常大的帮助

机器学习论文编写时绘图的模板

本人收集的机器学习论文，都是英文的，共161篇，供有需要的童鞋参考。