Linked Questions

2
votes
1answer
612 views

SVM: intuition behind maximizing the margin [duplicate]

I do understand that SVM is about finding the classifier that maximize the margin. But what is the intuition there? Please don't go into the math. Thx More specifically, if someone ask you during an ...
0
votes
0answers
519 views

Mathematical formulation SVM Model [duplicate]

Good morning, For a homework I used a support vector machines (classification) with a RBF kernel, k-fold cross validation=10, cost of constraints violation=100, and gamma=0.001. It works great and ...
1
vote
2answers
245 views

SVM mathematical background [duplicate]

I try to get the basic understanding behing SVM algorithm, however I have a problem with basic mathematics. I follow the lecture Support Vector Machine. Suppose the two classes can be separated by ...
1
vote
0answers
93 views

what is weight vector and bias in svm [duplicate]

I'm trying to understand the SVM algorithm but not able to understand what weight vector and bias is ? Could anyone explain it in laymen terms.
0
votes
0answers
25 views

What is the math behind predict() in e1071 for SVM? [duplicate]

I have no math or computer science training. When I run predict(svm,data,type="class") R spits out a prediction of 1 or 0 for each row of data. What is it doing ...
155
votes
8answers
247k 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 ...
100
votes
4answers
76k 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?
51
votes
5answers
90k 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): ...
79
votes
1answer
8k 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. ...
50
votes
4answers
67k 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 ...
58
votes
4answers
37k views

Why bother with the dual problem when fitting SVM?

Given the data points $x_1, \ldots, x_n \in \mathbb{R}^d$ and labels $y_1, \ldots, y_n \in \left \{-1, 1 \right\}$, the hard margin SVM primal problem is $$ \text{minimize}_{w, w_0} \quad \frac{1}{2} ...
38
votes
4answers
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?
34
votes
2answers
44k views

Which search range for determining SVM optimal C and gamma parameters?

I am using SVM for classification and I am trying to determine the optimal parameters for linear and RBF kernels. For the linear kernel I use cross-validated parameter selection to determine C and for ...
23
votes
1answer
21k views

What function could be a kernel?

In the context of machine learning and pattern recognition, there's a concept called Kernel Trick. Facing problems where I am asked to determine whether a function could be a kernel function or not, ...
20
votes
3answers
3k views

Applying the “kernel trick” to linear methods?

The kernel trick is used in several machine learning models (e.g. SVM). It was first introduced in the "Theoretical foundations of the potential function method in pattern recognition learning" paper ...

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