svm | 易学教程

python sklearn non linear svm penalty

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问题 I am using Python 2.7 with sklearn and using sklearn.svm.SVC with rbf kernel and suffer from over fitting. I tried using the C and Gamma as explained here and it did not do the trick If I understand correctly C and gamma are not l1 and l2 penalty, because C is the penalty for classifying wrong and gamma is the generalization parameter with respect to the data samples. i am looking for something that will penalize the model for complexity like l1 and l2. i want to use regularization and l1 or

Replication of scikit.svm.SRV.predict(X)

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问题 I'm trying to replicate scikit-learn's svm.svr.predict(X) and don't know how to do it correctly. I want to do is, because after training the SVM with an RBF kernel I would like to implement the prediction on another programming language (Java) and I would need to be able to export the model's parameters to be able to perform predictions of unknown cases. On scikit's documentation page, I see that there are 'support_ and 'support_vectors_ attributes, but don't understand how to replicate the

OpenCV SVM train_auto Insufficient Memory

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问题 This is my first post here so I hope to be able to ask my question properly :-) I want to do "elephant detection" by classifying color samples (I was inspired by this paper). This is the pipeline of my "solution" until the training of the classifier: Loading a set of 4 training images (all containing an elephant), and then splitting them in two images: one containing the environment surrounding the elephant (the "background"), and one containing the elephant (the "foreground"); Mean shift

支持向量机（SVM）处理非线性函数

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核心是通过一个优化函数，实现从高维到低维的转换。用数学表达式进行定义：最小化：其中γ是学习率learning_rate 限制条件：接下来用一个低维到高维的矩阵进行描述：有其中式子1.2属于C1，3,4属于C2 由此，通过一个优化函数，我们将非线性的函数变为了线性的函数，之后按照线性函数继续进行处理即可。优化函数，我们称作核函数，常用的核函数有高斯核、多项式核 sigmoid函数多项式核 —————————————————————————————— 公式编辑器真的不好用，用Word编辑之后放过来好难看。讲究这看吧！来源： CSDN 作者： wx-咸鱼链接： https://blog.csdn.net/weixin_45885232/article/details/103949224

Plotting 3D Decision Boundary From Linear SVM

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问题 I've fit a 3 feature data set using sklearn.svm.svc(). I can plot the point for each observation using matplotlib and Axes3D. I want to plot the decision boundary to see the fit. I've tried adapting the 2D examples for plotting the decision boundary to no avail. I understand that clf.coef_ is a vector normal to the decision boundary. How can I plot this to see where it divides the points? 回答1: Here is an example on a toy dataset. Note that plotting in 3D is funky with matplotlib . Sometimes

Plotting 3D Decision Boundary From Linear SVM

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text classification methods? SVM and decision tree

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问题 i have a training set and i want to use a classification method for classifying other documents according to my training set.my document types are news and categories are sports,politics,economic and so on. i understand naive bayes and KNN completely but SVM and decision tree are vague and i dont know if i can implement this method by myself?or there is applications for using this methods? what is the best method i can use for classifying docs in this way? thanks! 回答1: Naive Bayes Though this

How to train an SVM with opencv based on a set of images?

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问题 I have a folder of positives and another of negatives images in JPG format, and I want to train an SVM based on that images, I've done the following but I receive an error: Mat classes = new Mat(); Mat trainingData = new Mat(); Mat trainingImages = new Mat(); Mat trainingLabels = new Mat(); CvSVM clasificador; for (File file : new File(path + "positives/").listFiles()) { Mat img = Highgui.imread(file.getAbsolutePath()); img.reshape(1, 1); trainingImages.push_back(img); trainingLabels.push

How to train an SVM with opencv based on a set of images?

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机器学习——svm应用

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本章着重对算法部分进行讲解，原理部分不过多叙述，有兴趣的小伙伴可以自行查阅其他文献/文章一、什么是svm 支持向量机（Support Vector Machine, SVM）是一类按监督学习（supervised learning）方式对数据进行二元分类的广义线性分类器（generalized linear classifier），其决策边界是对学习样本求解最大边距超平面（maximum-margin hyperplane）。 1、支持向量与超平面在了解svm算法之前，我们首先需要了解一下线性分类器这个概念。比如给定一系列的数据样本，每个样本都有对应的一个标签。为了使得描述更加直观，我们采用二维平面进行解释，高维空间原理也是一样。举个例子，假设在一个二维线性可分的数据集中，如下图所示，我们要找到一条线（称为超平面）把两组数据分开，这条直线可以是图中的直线H 1 ，也可以是直线H 2 ，或者H 3 ，但哪条直线才最好呢，也就是说哪条直线能够达到最好的分类效果呢？那就是一个能使两类之间的空间大小最大的一个超平面，即图二中的gap在两个分类之间所占空间最大。这个超平面在二维平面上看到的就是一条直线，在三维空间中就是一个平面，高纬度下以此类推，因此，我们把这个划分数据的决策边界统称为超平面。离这个超平面最近的点就叫做支持向量，点到超平面的距离叫间隔