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DataFitter.tar
- 使用多项式进行数据拟合。手动选择多项式项数。 注意，项越多拟合越精确，但却会产生更复杂的结果，导致不能有效预测未来的数据（机器学习的内容）-Data using polynomial fitting. Manually select the number of polynomial terms. Note that the more fit the more precise term, but will produce more complex results, leading to the
meanS3VM
- MissSVM是一揽子解决多实例使用半监督支持向量机的学习问题。MissSVM目的是显示，如果假设IID实例，多实例学习可以作为一个半监督学习的特殊情况来看，可能会合并成半的领域和多实例学习领域监督学习。 因此，未来的多实例学习研究应只承担IID袋，避免IID实例假设。 -MissSVM package solution is to use multi-instance semi-supervised support vector machine learning problems. MissS
Ada_Boost
- AdaBoost, short for Adaptive Boosting, is a machine learning algorithm, formulated by Yoav Freund and Robert Schapire. It is a meta-algorithm, and can be used in conjunction with many other learning algorithms to improve their performance.
Xinying_Ph.D.thesis
- SUPPORT VECTOR MACHINE IN CHAOTIC HYDROLOGICAL TIME SERIES FORECASTING 支持向量机混沌时间序列预测-This research attempts to demonstrate the promising applications of a relatively new machine learning tool, support vector machine, on chaotic hyd
Digit
- Java实现的手写数字识别工具，基于人工神经网络和机器学习原理。先读入一个文件进行训练，然后可以识别相同格式文件中的手写数字。目录下的PNT文件即为手写数字的数据文件。-Java implementation of the handwritten numeral recognition tool, based on artificial neural networks and machine learning principles. To read a file into training, t
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
SVM-Multiregression
- SVM Multiregression for Non Linear Channel Estimation in Multiple-Input Multiple-Output Systems 在多输入多输出系统中的SVM多元回归非线性逼近-This paper addresses the problem of Multiple-Input Multiple-Output (MIMO) frequency non-selective channel estimation. We d
incremental_svm
- 这是对Incremental Support Vector Machine Learning的C++实现。其中提供了测试数据供使用者入手。-This is the Incremental Support Vector Machine Learning for C++ implementation. Which provides test data for the user to start with.
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
SVMPCA
- 在机器学习中经常会用到支持向量机，该代码是再使用支持向量机前对数据进行主成分分析的代码-We often meet support vector machine in machine learning , the code is about the principal component analysis befor using support vector machine (SVM).
genetic-algorithm-introduction
- 这是关于遗传算法基本理论的大致介绍 机器学习-This is about the basic theories of genetic algorithm introduced roughly machine learning
algorithm_of_BP_improve_alph_learn
- 机器学习中BP算法的一点改进，完整版，包含训练集和测试集。-Machine learning point improvement in the BP algorithm, the full version, including the training and testing sets.
untitled
- 利用opencv实现机器学习，K近邻分类器的小例子-Achieved using machine learning opencv, K small example of neighbor classifier
Introduction-to-Probability-(MIT-2000-371s)
- A book describing basic probability needed for fundamental CS theory in machine learning and other topics.
Contact-lens-wear-problems
- 该实验采用机器学习方法中的朴素贝叶斯算法用于信息分类，问题的背景是根据一个人的年龄，视力缺陷，散光和泪腺分泌情况决定其是否可佩戴隐形眼镜。-Using machine learning to solve contact problems
AdaBoost_AD_AdaBoost
- 提出了一种改进的Adaboost学习方法，adaboost是一种常用的机器学习方法，有很大的应用-Proposed an improved Adaboost learning method, adaboost is a commonly used machine learning methods, a great application。
MachinePLearning[TomPM.PMitchell]
- 学习机器学习的好书。Tom.M.Mitchell著,中文版,由曾华军等人翻译此书非常好,把BP原理讲得非常透彻,由其是误差的公式推导,比别的书都说得清楚一点-Learning machine learning books. Tom.M.Mitchell, Chinese edition, by Ceng Huajun et al. Translate the book is very good, the principle of BP speaks very thorough, the erro
beiyes-net
- 最新的机器学习方法相关文章，即贝叶斯网学习经典算法文章-The latest machine learning methods related to the article, that article classical algorithm for Bayesian network learning
seminarpapers
- semiars The World Wide Web, which has started as a document repository, is rapidly transforming to a full fledged virtual environment that facilitates services, interaction, and com- munication. Under this light, the Semantic Web and Web 2.
Data_Mining
- Weka同步课本-Data_Mining_Practical_Machine_Learning_Tools_and_Techniques_3rd_Edition-Mantesh-Data Mining Practical Machine Learning