搜索资源列表
gentleboost
- Gentleboost，这是一种基于信息融合的机器学习源代码，成功应用于多个工程信息领域。-Gentleboost, which is a fusion-based machine learning source code, successfully applied in various projects in the field of information.
rosetta-1[1].0.1
- ROSETTA C++库是一个C++类库和例程集合，支持基于可识别性的经验建模和数据挖掘。它由许多用于通用机器学习和粗糙集理论的例程组成。 -ROSETTA the C storehouse is a C kind of storehouse and the example regulation set, supports based on 鍙
SNoW_v3.2.1.tar
- 基于稀疏网络的精选机器学习模型，相比SVM有更快和更精确的学习效果。-Selected based on the sparse network machine learning model, compared with SVM are faster and more accurate learning.
INFORMATION_THEORETIC_LEARNING
- 模式识别方面的文章，基于瑞利熵的机器学习方法，以及应用举例，建议做MMI的同志学习。-Pattern Recognition in the article, based on Rayleigh entropy machine learning methods, and application examples, it is recommended to do MMI comrades to learn.
深度学习框架
- 基于windows的深度学习框架，特别适合初学者学习。
机器学习实战及配套代码
- 基于python的机器学习教程，有代码，方便学习(Python based machine learning, there are code to facilitate learning)
基于机器学习的时间序列预测关键技术研究
- 利用ELM进行预测，配合震荡盒理论的交易策略的一个交易系统设置。(A trading system is set up by using ELM to predict the trading strategy in conjunction with the shock box theory.)
dollar
- 基于$1和opencv机器学习方法的图像识别(Image recognition based on machine learning method based on $1 and opencv)
abu-master(2)
- 机器学习算法进行数据分析，基于python语言(Data analysis by machine learning algorithm)
基于MATLAB的股票估价模型设计
- 量化股票交易模型，投资学习利器，基于机器学习，深度学习，智能化投资(Quantitative stock trading model)
ELM分类器
- ELM是基于深度学习的分类器，运算速度快。 在B_data.m里导入待分类矩阵B.mat（1-n列为特征值，n列为标签）；运行B_data.m；再打开fuzzyEn_main.m并运行即可。(ELM is based on depth learning classifier, computing speed. In B_data.m imported matrix to be classified B.mat (1-n as eigenvalues, n as a label); Run B
基于机器学习的手写数字识别
- 基于Python机器学习的手写数字识别 基于Python机器学习的手写数字识别(Handwritten digit recognition based on Python machine learning Handwritten digit recognition based on Python machine learning)
现代机器学习基于深度学习的图像特征提取
- 现代基于深度学习的图像特征提取，比较不错的文档(On the system of an unmanned intelligent vehicle with)
Machine Learning
- 作者是Prateek Joshi.人工智能专家。本书注重于对基于机器学习中深度学习内容的分析，并附上了许多经典案例，非常值得一读。(The writer is an expert in Prateek Joshi. AI. This book focuses on the analysis of in-depth learning in machine-based learning, and attaches many classic cases, which are worth reading
基于机器学习的WiFi高精度定位研究_邱爱昆
- 能够有效的利用ai技术进行WiFi室内定位(WiFi Indoor Location Using AI Technology Effectively)
机器学习实战：基于Scikit-Learn和TensorFlow
- 基于scikit-learn和tensorflow的机器学习实战教程(Machine Learning Practical Course)
shipclass
- 基于机器学习的舰船目标距离像自动识别技术(Automatic recognition technology of ship target range profile based on machine learning)
机器学习项目班-金融反欺诈
- 基于Python的金融反欺诈实战练习，附PPT讲解、源码及数据集(Practice of financial anti fraud based on python, with PPT explanation, source code and data set)
机器学习实战书+源代码
- 机器学习横跨计算机科学、工程科学和统计学等多个学科，需要多学科的专业知识。在需要解释并操作数据的领域都或多或少可以运用到机器学习，通过这本书可以系统地学习基于python语言的机器学习的相关知识(Machine Learning in Action written by Peter Harringto. Machine learning covers many subjects, such as computer science, engineering science and statisti
[2]基于机器学习的室内定位算法研究_周杰
- 通过机器学习的方法来进行室内定位的算法研究(The algorithm of indoor location is studied by machine learning)