# Questions tagged [svm]

Support Vector Machine refers to "a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis."

222 questions
Filter by
Sorted by
Tagged with
63k views

### How does a Support Vector Machine (SVM) work?

How does a Support Vector Machine (SVM) work, and what differentiates it from other linear classifiers, such as the Linear Perceptron, Linear Discriminant Analysis, or Logistic Regression? * (* I'm ...
8k views

### How to know if a learning curve from SVM model suffers from bias or variance?

I created this learning curve and I want to know if my SVM model suffers from bias or variance? How can I conclude that from this graph?
18k views

### How to prove that the radial basis function is a kernel?

How to prove that the radial basis function $k(x, y) = \exp(-\frac{||x-y||^2)}{2\sigma^2})$ is a kernel? As far as I understand, in order to prove this we have to prove either of the following: For ...
238k views

### What is the influence of C in SVMs with linear kernel?

I am currently using an SVM with a linear kernel to classify my data. There is no error on the training set. I tried several values for the parameter $C$ ($10^{-5}, \dots, 10^2$). This did not ...
7k views

### Help me understand Support Vector Machines

I understand the basics of what a Support Vector Machines' aim is in terms of classifying an input set into several different classes, but what I don't understand is some of the nitty-gritty details. ...
29k views

### What algorithms need feature scaling, beside from SVM?

I am working with many algorithms: RandomForest, DecisionTrees, NaiveBayes, SVM (kernel=linear and rbf), KNN, LDA and XGBoost. All of them were pretty fast except for SVM. That is when I got to know ...
17k views

### Feature map for the Gaussian kernel

In SVM, the Gaussian kernel is defined as: $$K(x,y)=\exp\left({-\frac{\|x-y\|_2^2}{2\sigma^2}}\right)=\phi(x)^T\phi(y)$$ where $x, y\in \mathbb{R^n}$. I do not know the explicit equation of $\phi$. I ...
74k views

### How to select kernel for SVM?

When using SVM, we need to select a kernel. I wonder how to select a kernel. Any criteria on kernel selection?
10k views

### How can SVM 'find' an infinite feature space where linear separation is always possible?

What is the intuition behind the fact that an SVM with a Gaussian Kernel has inﬁnite dimensional feature space?
67k views

### Linear kernel and non-linear kernel for support vector machine?

When using support vector machine, are there any guidelines on choosing linear kernel vs. nonlinear kernel, like RBF? I once heard that non-linear kernel tends not to perform well once the number of ...
13k views

### Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible

I am trying to understand the intuition behind kernel SVM's. Now, I understand how linear SVM's work, whereby a decision line is made which splits the data as best it can. I also understand the ...
87k views

### How does one interpret SVM feature weights?

I am trying to interpret the variable weights given by fitting a linear SVM. (I'm using scikit-learn): ...
65k views

### Comparing SVM and logistic regression

Can someone please give me some intuition as to when to choose either SVM or LR? I want to understand the intuition behind what is the difference between the optimization criteria of learning the ...
50k views

### SVM, Overfitting, curse of dimensionality

My dataset is small (120 samples), however the number of features are large varies from (1000-200,000). Although I'm doing feature selection to pick a subset of features, it might still overfit. My ...
5k views

### How to calculate decision boundary from support vectors?

I want to obtain decision boundary of SVM using OpenCV 2.4.11, but it seems that it's not returning it explicitly, but only support vectors. How we can calculate decision boundary from support ...
76k views

### How to intuitively explain what a kernel is?

Many machine learning classifiers (e.g. support vector machines) allow one to specify a kernel. What would be an intuitive way of explaining what a kernel is? One aspect I have been thinking of is ...
36k views

7k views

### Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?

Why do people use Quadratic Programming techniques (such as SMO) when dealing with kernelized SVMs? What is wrong with Gradient Descent? Is it impossible to use with kernels or is it just too slow (...
894 views

### KKT in a nutshell graphically

Objective Confirm if the understanding of KKT is correct or not. Seek for further explanation and confirmations on KKT. Background Trying to understand KKT conditions, especially the complementary ...
4k views

### Given a set of points in two dimensional space, how can one design decision function for SVM?

Can someone explain me how one goes about designing a SVM decision function? Or point me to resource that discusses a concrete example. EDIT For the below example, I can see that the equation \$X_2 = ...
9k views

### Which performance measure to use when using SVM: MSE or MAE?

It is a common practice to measure an SVM model's performance by calculating its MSE (Mean Square Error). Why not use Mean Absolute Error (averaging errors' absolute values instead of squared values)?
13k views

### Difference between the types of SVM

I am new to support vector machines. Short explanation The svm function from the e1071 package in R offers various options: ...
14k views

### What does the cost (C) parameter mean in SVM?

I am trying to fit a SVM to my data. My dataset contains 3 classes and I am performing 10 fold cross validation (in LibSVM): ...
9k views

### About SVM cost and gamma parameters tuning

I am using R and e1071 package to tune a C-classification SVM. My question is: regardless of the kernel type (linear, ...
31k views

### Why scaling is important for the linear SVM classification?

When performing the linear SVM classification, it is often helpful to normalize the training data, for example by subtracting the mean and dividing by the standard deviation, and afterwards scale ...
4k views

### What is a kernel and what sets it apart from other functions

There seem to be many machine learning algorithms that rely on kernel functions. SVMs and NNs to name but two. So what is the definition of a kernel function and what are the requirements for it to be ...
7k views

### Best way to handle unbalanced multiclass dataset with SVM

I'm trying to build a prediction model with SVMs on fairly unbalanced data. My labels/output have three classes, positive, neutral and negative. I would say the positive example makes about 10 - 20% ...