Support Vector Machine refers to "a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis."
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Why can the margin of SVM be approximated by 1?
The separate function of SVM is :
$wx+b=0$
The function distance of support vector to the separate plane is :
$|r| = wx_i+b$
And we can normalize the $w$, then the distance can be write as :
...
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2answers
26 views
How to interpret the model parameters of libsvm via MATLAB interface?
I used the MATLAB interface of libsvm for doing binary classification of 997-dimensional training data. I am trying to understand how the resulting model is used to compute the predicted output (which ...
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1answer
21 views
How do I identify which parameters are correlated to binary output?
I'm trying to program an SVM in Python to categorize proteins as "Go" or "No-Go".
I have a list of about 30 proteins, each with ~ 100 columns of structure-related parameters and 1 column of "True" or ...
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2answers
120 views
When there are many more failures than successes should I let classes be equal in SVM?
I have about 5544 runs where I am trying to classify it as failure or success. Here the number of runs that lead to failure is only 64 and rest is sucess. In that case when I try to use SVM should I ...
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1answer
173 views
Training an SVM classifier with non-negative weight constraint
I have a problem, where I need to learn a classifier (such as SVM) such that all the learned weights to be non-negative due a constraint on the classifier function.
I found out that "SVM Struct" is ...
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1answer
52 views
Kernel selection intuition
Which problems are best solved using which kernels and why?
Can you give a simple toy problem that isn't linearly separable in input space but is linearly separable in feature space, using an RBF ...
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1answer
34 views
Checking autocorrelation in non-parametric methods
I would like to forecast short-term electric load by using Artificial Neural Network and Support Vector Regression. However, there's one question that sticks in my mind. In such forecasting with ...
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1answer
96 views
What data from MATLAB's svmstruct are needed for classification in a different language?
As the title already states Iam wondering what data exactly are needed from the MATLAB svmstruct to be able to classify a new instance outside of MATLAB, e.g. in ...
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36 views
SVM classifier (with soft-margin) implementation in R, gamma value and quadprog
I'm trying to implement a Support Vector Machine classifier in R and I have to solve the optimization problem using the quadprog R package which solves problems of the form :
$$min_b \frac{1}{2} ...
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1answer
44 views
regularized logistic regression and support vector machine
L2 regularized logistic regression differs with L2 regularized support vector machine with their loss function. Are there more deep differences for these two models? I tried several data sets, and ...
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1answer
165 views
Different prediction score for two SVM-based classifiers
As a validation study, I use two libsvm-based svm classifier against the same data set.
One classifier is libsvm implementation in Rapidminer. Another classifier is Libsvm itself. Both of them assume ...
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10 views
nu-based Support Vector Machine (SVM): limit on nu for 1-class SVM?
For a nu-based SVM, there are limits on acceptable nu (soft margin) parameters (p. 126 of Appl. Stochastic Models Bus. Ind., 2005; 21:111–136).
Are there similar limits in the case of a 1-class SVM?
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1answer
33 views
How to understand effect of RBF SVM
How can I understand what the RBF Kernel in SVM does? I mean I understand the maths, but is there a way to get a feeling when this kernel will be useful?
Would results from kNN be related to SVM/RBF ...
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1answer
55 views
The Lagrange multipliers of SVM
Actually the solve the SVM is to solve the following Lagrangian Equation:
If we don't use kernel function, $\langle x^{(i)},x^{(j)}\rangle$ is just the vector vector inner product. The ...
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2answers
81 views
Support Vector machine : a simple question
I think that a formulation of SVM for points x with label y is :
$$
\begin{align}
\arg\min_{\substack{u,w,b}} \frac{1}{2} \cdot |w|^2 + C \cdot \sum_{i} u_i \\
s.t.\ \ y_i\cdot (w \cdot x_i + b) ...
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1answer
145 views
Using Adaboost for feature selection?
Is it okay to use Adaboost to do feature selection (selecting a subset of dimensions $S$ from a high-dimensional feature vector $V$)?
I divided the samples into four non-overlapping sets: $A$ ...
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1answer
78 views
When does Naive Bayes perform better than SVM?
In a small text classification problem I was looking at, Naive Bayes has been exhibiting a performance similar to or greater than an SVM and I was very confused.
I was wondering what factors decide ...
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36 views
Which Regression methods are suitable for binary valued features and continuous output?
I want to build a machine learning model to regression on continuous output given binary valued features(0,1). the dimension of my problem is around 200. which of the flowing methods seems suitable ...
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1answer
22 views
Bias term in support vector machine
In SVM, there is a bias term. But looks to me there are very few discussions on the physical meanings of this term. Why should we have that? How does this term affect the model?
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42 views
How can one setup a linear support vector machine in excel?
Through the last year I have been working with support vector machines for a binary text classification task. Having used software as R and Rapidminer I have not spend much time on understanding what ...
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8 views
Are support vectors from the same class always equally geometrically important?
Are support vectors from the same class always given equal importance / Lagrange-multiplier weight?
The paper called "Duality and Geometry in SVM Classifiers" ...
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2answers
787 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 ...
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13 views
Placement of positive and negative support vectors with respect to the origin
In all of the graphics I have seen on the hyperplanes of support vector machines, the positive class (+1s) is away from the origin, while the negative class (-1s) is closer to it. Is this always true?
...
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21 views
Pegasos algorithm parameters estimation
For learning pourpose I'm testing my own implementation of the Pegasos algorithm and I'm getting a quite high error rate.
My dataset contains 20k examples and I'm using 17,5k of them for the training ...
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3answers
177 views
Why is svm not so good as decision tree on the same data?
I am new to machine learning and try to use scikit-learn(sklearn) to deal with a classification problem. Both DecisionTree and SVM can train a classifier for this problem.
I use ...
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40 views
Image classifier in python for few samples
I have 150 pictures that represent archeological signs and 5 categories to which they belong. These pictures have features like circularity, roughness and elongation that are expressed as continuous ...
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1answer
80 views
SVM regression with longitudinal data
I have about 500 variables per patient, each variable has one continous value and is measured at three different time points (after 2 month and after 1 year). With the regression I would like to ...
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1answer
819 views
How to obtain decision boundaries from linear SVM in R?
I am in need of a package that can give me the equation for a linear SVM model. Currently I'm using e1071 like so:
...
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1answer
30 views
Evaluating features and similarity measures
I am currently developing a classificator, which is supposed to classify into a number of classes. For this purpose I am
designing some features and similarity measures which I might use for a later ...
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1answer
110 views
How to determine if the data points are linearly separable from an SVM hyperplane
How to know the data points are linearly separable from an SVM hyperplane?
How to get the optimal classifier during iteration process?
How to calculate the complexity of the SVM model?
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22 views
Differences between SVR with a linear kernel and linear least squares
I've been working on a toy problem of predicting reviews a product will get in the future. I found that SVR with a linear kernel worked better than doing a linear least squares regression on the data ...
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30 views
Predicting with Relevance Vector Machines
I am trying out this Matlab toolbox for Relevance Vector Machines by Tipping: http://www.miketipping.com/sparsebayes.htm
This has an implementation of Relevance Vector Machines, and generates pretty ...
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3answers
61 views
Order of Support Vectors, and how to reduce them
I am working in an extremely memory constrained environment, and the number of support vectors my Matlab design is generating is just not something that scales. That led me to move to finding a way to ...
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1answer
54 views
What exactly is the equation for SVM classification for new example?
I understand that in the case of Logistic Regression, we simply multiply our weights with Input example for classification. But what exactly is the equation that we calculate in the case of SVM to ...
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1answer
49 views
Is this kernel proper?
I think it is proper as it follows the rule that K(x1,x2) = f(x1)f(x2)
Also they are both a function of only one of the points in the kernel respectively. Finally the product of two valid kernels ...
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1answer
67 views
LIBSVM scaling questions
One of the most commonly misused statistical technique is scaling. Most of the time, we normalize the data with the knowledge that we don't process in the first place from training data. I recently ...
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2answers
47 views
In SVM, what are the labels and how do you get them from the data?
I'm working on a school project and have decided to use SVM for stock market prediction. I have a 1000x5 matrix of stock quotes containg data for open, close, high, low, volume data.
From what I ...
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22 views
Weighted SVM and Up-sampling
I'm performing classification with the libSVM package in R and am wondering about the correct procedure for weighting or up-sampling. I have a data set that is 19,396 observations of which only 81 are ...
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1answer
122 views
Is it possible to compare two feature selections algorithms by cross-validations?
Assume I have two feature selection algorithms, A and B, which are developed based on SVM. I applied these two algorithms on the same dataset, a Liver Cancer dataset (400 features & 150 samples), ...
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2answers
57 views
Highly unbalanced test data set and balanced training data in classification
I have a training set with about 3000 positive instances and 3000 negative instances. But my test data set is pretty much un-balanced. The positive set only has 50 instances and negative has 1500 ...
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1answer
36 views
Linear SVM and Random oversampling
Considering class imbalance, why does random oversampling, in general, improve the performance of a linear SVM?
Is it because the number of support vectors for the minority class are increased as a ...
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56 views
Probability output from support vector machine (svm) with soft margin
Based on my very simple understanding of SVMs, it seems like a probabilistic output would be a very useful thing to have. Soft margin seems to part of the way toward accounting for noisy data, but ...
2
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142 views
Example of classification dataset where SVM with linear kernel performs well
I'm looking for a dataset (preferably with a story, at any rate a real dataset)
where a SVM with a linear kernel performs well...in other words i'm looking for
a dataset where the class boundary is ...
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42 views
Energy estimation through machine learning
Greedings to everybody.
I have the dataset which you can find here, containing many different characteristics of different houses, including their types of heating, or the number of adults and ...
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1answer
17 views
in nonlinear binary classification problems, which is the optimal dimension for make it lineary separable?
My question pertains to linear separability with hyperplanes in a support vector machine.
Is posible to determinate the optimal dimension in which i have to transform a training data set for make it ...
3
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2answers
233 views
SVM prediction sensitivity when compared to neural networks and logistic regression
Basically I want to classify a rather rare status (about 2% of the 2000) with some predictors. I have used logistic regression, neural network, and Support Vector Machines to do it.
All the ...
2
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1answer
83 views
Mixed SVM kernel of RBF and linear
I've read some introduction about different kernels for SVM. It seems RBF is a measure of point distance while the basic kernel (i.e. no kernel) splits the space by hyper-planes.
I could imagine that ...
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17 views
confusion related to the dual of svm
I have a confusion related to the dual of svm
In the main objective function I have
Now to solve the dual of this objective function, I will minimize with respect to the primal variables first to ...
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23 views
kernels distances gram matrix classification
Could you please explain some thing about kernels? As I understand it is technique to map the feature space into a high dimensional feature space where we could separate two classes by a linear ...
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26 views
Confusion related to L2 and L1 SVM
I have this confusion related to L1 and L2 svm. I was reading this paper
I am attaching the screenshot and the part I didn't understand
The part that I didn't understand how it was derived
I ...
