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https://blog.csdn.net/qq_36330643/article/details/77574587
2017-8-25 · SVM - support vector machine, 俗称支持向量机，为一种supervised learning算法，属于classification的范畴。在数据挖掘的应用中，与unsupervised的Clustering相对应和区别。https://blog.csdn.net/qq_36330643/article/details/77574587
https://www.jianshu.com/p/31ec24baaada
Support Vector Machine 支持向量机，一个有监督的机器学习算法。通俗来讲，它是一种二类分类模型，其基本模型定义为特征空间上的间隔最大的线性分类器，其学习策略便是间隔最大化，最终可转化为一个凸二次规划问题的求解。https://www.jianshu.com/p/31ec24baaada
https://ww2.mathworks.cn/discovery/support-vector-machine.html
2020-1-24 · A support vector machine (SVM) is a supervised learning algorithm that can be used for binary classification or regression. Support vector machines are popular in applications such as natural language processing, speech and image recognition, and computer vision. ...https://ww2.mathworks.cn/.../support-vector-machine.html
https://www.datasciencelearner.com/hyperparameters-for-the-support-vector-machines/
2 days ago · Support Vector Machine is one of the popular machine learning algorithms. If you have earlier build the machine learning model using a support vector machine, then this tutorial is for you. You will learn how to optimize your model accuracy using the SVM() parameters. In this intuition, you will know how to find the best hyperparameters for the ...https://www.datasciencelearner.com/hyperparameters-for-the-support-vector-machines
http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf
2006-11-9 · Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston NEC Labs America 4 Independence Way, Princeton, USA. [email protected]/~kathy/cs4701/documents/jason_svm_tutorial.pdf
https://zhuanlan.zhihu.com/p/24638007
2018-5-8 · SVM的全称是Support Vector Machine，即支持向量机，主要用于解决模式识别领域中的数据分类问题，属于有监督学习算法的一种。SVM要解决的问题可以用一个经典的二分类问题加以描述。https://zhuanlan.zhihu.com/p/24638007
http://cs229.stanford.edu/notes/cs229-notes3.pdf
2018-10-22 · Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. SVMs are among the best (and many believe are indeed the best) “off-the-shelf” supervised learning algorithms. To tell the SVM story, we’ll need to first talk about margins and the idea of separating data with a large “gap.”cs229.stanford.edu/notes/cs229-notes3.pdf
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