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SVM多分类问题
说明： 支持向量机(SVM)——分类预测，多分类问题。(Support vector machine (SVM) - classification prediction, multi classification problem.)
- 2021-02-28 11:09:35下载
- 积分：1
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inpaint-object-remover-python
实现基于样本块的图像修复，是Criminisi经典图像修复算法。(Criminisi algorithm about exemplar-based inpainting)
- 2020-11-06 14:49:50下载
- 积分：1
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机器学习决策树2个经典案例
机器学习决策树
- 2019-05-10下载
- 积分：1
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熵权法
熵权法赋权步骤
1）数据标准化
2）计算第j项指标下，第i个评价对象的特征比重pij
3）计算第j项指标的熵值Ej
4）计算第j项指标的差异系数gj
5）确定各指标的熵权
6）分别计算各个评价对象的综合评价值
- 2022-10-14 05:10:04下载
- 积分：1
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OUROMAv2.3_CL.py
说明： ABAQUS 脚本生成节点信息，计算J积分(J-Integral calculation using ABAQUS)
- 2021-04-08 17:19:00下载
- 积分：1
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Learning Data Mining with Python
说明： Python数据挖掘入门与实践
本书作为数据挖掘入门读物，介绍了数据挖掘的基础知识、基本工具和实践方法，通过循序渐进地讲解算法，带你轻松踏上数据挖掘之旅。本书采用理论与实践相结合的方式，呈现了如何使用决策树和随机森林算法预测美国职业篮球联赛比赛结果，如何使用亲和性分析方法推荐电影，如何使用朴素贝叶斯算法进行社会媒体挖掘，等等。本书也涉及神经网络、深度学习、大数据处理等内容。本书面向愿意学习和尝试数据挖掘的程序员。(Introduction and practice of Python data mining
As a primer on data mining, this book introduces the basic knowledge, basic tools and practical methods of data mining. By step-by-step explanation of the algorithm, you can easily embark on the journey of data mining. This book uses a combination of theory and practice, showing how to use the decision tree and random forest algorithm to predict the results of the American professional basketball league game, how to use the affinity analysis method to recommend movies, how to use the naive Bayesian algorithm for social media mining ,and many more. This book also covers neural networks, deep learning, big data processing and more. This book is for programmers who are willing to learn and experiment with data mining.)
- 2019-05-19 03:08:27下载
- 积分：1
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ODL_Jonas_Adler_MIC_SW2015
说明： ODL python数据处理，用于医学影像重建，包括MLEM、FBP算法等(ODL Python data processing for medical image reconstruction, including MLEM, FBP algorithm, etc)
- 2020-11-30 21:09:29下载
- 积分：1
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PythonInventYouOwnComputerGameswithPython
说明： python游戏编程教材 人民邮电出版社第三版(Python game programming textbook, people's Posts and Telecommunications Press, Third Edition.)
- 2018-04-03 20:58:01下载
- 积分：1
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MachineLearning
说明： 机器学习的鸢尾花数据集的应用逻辑回归算法分类(Classification of iris data set by logical regression algorithm)
- 2021-03-22 13:59:32下载
- 积分：1
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lesson51-WGAN实战
说明： 生成式对抗网络（GAN, Generative Adversarial Networks ）是一种深度学习模型，是近年来复杂分布上无监督学习最具前景的方法之一。模型通过框架中（至少）两个模块：生成模型（Generative Model）和判别模型（Discriminative Model）的互相博弈学习产生相当好的输出。(Emergent against network (GAN, Generative Adversarial Networks) is a kind of deep learning model, is a complex distribution in recent years on one of the most promising method for unsupervised learning.
The frame of the Model by (at least) two modules: generation Model (Generative Model) and the discriminant Model (Discriminative Model) of the game to learn each other produce fairly good output.)
- 2019-10-16 19:59:06下载
- 积分：1
