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Machinelearning
- 机器学习，模式识别方向相关论文，有兴趣的同学可以下来看~-Machine learning, pattern recognition, the direction of the relevant papers, students who are interested can look under ~
MLcode
- 这是关于机器学习的经典例程，只有你想不到的，没有你做不到的。即可用来学习，也可参考来写论文。包含几乎所有机器学习的相关内容。-This is the classic routines on machine learning, and only you can not think, no you can not do. Can be used to learn, but also can refer to write papers. Almost all machine learning cont
rotation_forest_A_New_Classifier_Ensemble_Method.z
- 论文提出了旋转森林算法，并将它与随机森林等其他机器前学习算法进行了比较-Rotating forest algorithm is proposed, and it and other machines and random forest learning algorithm were compared before
Kuschner-BayesianNetwork-feature
- Kuschner论文，贝叶斯网络方法在质谱数据特征选择。其中关于机器学习中贝叶斯分类器部分有完整原理分析，可以用于认知无线电网络的频谱感知等新领域。含有matlab程序大于100页，子函数很多。-Kuschner paper, Bayesian network methods of feature selection in mass spectrometry data. One of the Bayes classifier machine learning part of a complete
0000
- 关于研究新陈代谢网络的几篇论文，可用于神经网络，机器学习相关理论的研究-Study of metabolic networks of several papers, can be used for neural networks, machine learning theoretical research related to
Lucas-Kanade2
- Lucas-Kanade part 2 对计算机视觉和机器学习很有帮助 详细论文和代码在文章的附录中有详细地址 很不错 -Lucas-Kanade part 2 of the computer vision and machine learning for more helpful articles and code in the article addresses in detail in the appendix is very good
ai-robot
- 机器人人论文，强化学习，环境建立，仿人机器人等方向-Papers of robot people, strengthen the learning environment to establish a humanoid robot direction
Semsupervised-learning
- 这是一篇关于半监督的论文，半监督学习问题广泛存在于现实世界中, 已经成为目前机器学习和模式识别领域中的一个研究热点. 文章 综述了半监督学习问题的基本思想、研究现状、常用算法及其一些应用领域, 分析了目前存在的主要困难, 并指出需进一步研究的几个问题.-Sem-i supervised learning has been w idely used in the w orld and become a hot topic in the resear ch field of machine
ML2
- 本程序是基于流型标签的标签学习，很有机器学习参考价值，各位可以参考一下。代码中分享了论文来源。-The algorithm ML^2 includes three main steps. First, it estimates the topological structure the feature space. Then it transfers the topological structure to the label space and constructs the l
door-localizaion
- 论文，机器学习的应用，采用视觉定位门，实现机器人开门的操作。-The localization of door using vision to accomplish the robot door opening.
attentionFactorizaitonMachine
- Attension FM的理论论文，可用于高度稀疏数据场景，并且具有线性的计算复杂度(Factorization Machines (FMs) are a supervised learning approach that enhances the linear regres- sion model by incorporating the second-order fea- ture interactions)
伯克利大学机器学习(Practical Machine Learning)
- 伯克利大学机器学习相关资料，教程，论文，等(Berkeley University Machine Learning related Materials, courses, papers, etc.)