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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Spatial coordinates (latitude and longitude) are non significant

I want to use latitude and longitude as a feature for models like SVM or Logistic Regression (both for classification). What is the most common approach to use latitude and longitude values as ...
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20 views

Using Naive Bayes as vectorizer and SVM for classification in Java [on hold]

0 down vote favorite I am trying to classify legal case documents which are in text format, in different folders like Civil, Land, Criminal, e.t.c, I intended using Naive Bayes as Vectoriser to get ...
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5 views

find optimal parameters for SVM from tune() in R= [migrated]

I am optimizing the gamma and cost variable using the tune function ...
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8 views

assign asymmetric cost in SVM classification in R?

I know that we can set a symmetric cost in R for svm,how do we set an asymmetric cost? I want to do a grid optimization like the following: ...
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23 views

In soft-margin SVM, is it guaranteed that some points will lie on the margin?

In soft-margin SVM, once we solve the dual-form optimization problem (using quadratic programming, which is guaranteed to converge on a global optimum because the problem is convex), we get a vector ...
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6 views

kFold and defaul gamma in libsvm

I have a 1600x8 value table, trying to do a SVR. After a few validations, c=2048/gamma=2 have the smallest MSE in cross validation. With this parameters, the test part was not good as C=2048, and a ...
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19 views

How to find optimal penaltyparameter C for SVM (regression)

I am training an svm regressor using python sklearn.svm.SVR From the example given on the sklearn website, the above line of code defines my svm. ...
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29 views

SVM parameters clarification

James et al. in An introduction to the statistical learning (p. 351) claim that the solution to the support vector classifier problem involves only the inner products of the observations. They ...
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26 views

Output of Scikit SVM in multiclass classification always gives same label

I am currently using Scikit learn with the following code: ...
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44 views

Why does the linear SVM give a lot of support vectors?

I simulate a simple linear setup: n = 1000 X = runif(n) Y = runif(n) ind = X + 2*Y < 1 ind[ind == TRUE] = runif(sum(ind)) < 1 plot(X,Y,col = ind + 1) ...
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49 views

Multi-class Classification using SVM with PCA

I'm doing an image classification task and the number of features of each example image is pretty huge (3,072: # pixels in each image). I'm thinking of using PCA to reduce the # features of each image ...
0
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1answer
22 views

Issues plotting a fitted SVM model's decision boundary using ggplot2's stat_contour()

I'm trying to figure out how to plot a decision boundary for a fitted svm model in ggplot2. Right now, I'm attempting to do so by using stat_contour. Here is my code with an example call to my ...
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27 views

SVM Dual Formulation :: KKT Constraint

In Andew Ng's SVM course notes, the final hard margin optimization problem is given as the following: I am unclear how to see from this where the 5th constraint is satisfied. The definition of ...
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11 views

Support vector regression in weka

I am using SVR for statistical down-scaling of precipitation. I have taken the first 3 factor scores in principal component analysis of variables as predictors and precipitation as predictand. As ...
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1answer
45 views

In natural language processing (NLP), how do you make an efficient dimension reduction?

In NLP, it's always the case that the dimension of the features are very huge. For example, for one project at hand, the dimension of features is almost 20 thousands (p = 20,000), and each feature is ...
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1answer
20 views

Structural risk minimization and SVMs

I know what is SRM but I didn't understand the relation between SRM and SVMs. Can anyone explain me this? Why they say that SVMs rely on a SRM approach? Thank you so much!
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24 views

Intution on Interchangability of Regression and Classification

Dear Oracles of CrossValidated, I've been trying to gather intuition on the relationship between methods that seems to be escaping me. Can someone explain how regression and classification can be ...
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15 views

Predicting the near-future values using an unevenly sampled time-series data

Summary Need help with predicting the near-future values using an unevenly sampled time-series data. Data is collected as events, and is converted to time series. I have tried out a few approached ...
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7 views

How to deal with clustered features in classification

Imagine there are three classes of data, labeled A,B and C. I have separated the train set ...
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43 views

The role of $\gamma$ & $C$ in SVM

I'm using support vector machine method with the Gaussian kernel. Is it true that $\gamma$ and $C$ are hyper parameters of SVM? What is their role exactly?
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47 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
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20 views

Using SVD on features before SVM classification, when p >> N

So I am going through Hastie's Elements of Statistical computing, and in section 18.3.5 which deals with computational shortcuts when the number of dimensions $p$ is much larger that the number of ...
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1answer
24 views

Label propagation in semi-supervised learning

Suppose we have a set of labeled and unlabeled instances. 70%unlabeled 30% labeled. We apply a semi-supervised algorithm. Let's say we apply S3VM or Laplacian SVM. We use all the data available. When ...
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14 views

Optimal Margin Classifer : Optimization Problem Setup

In the notes from Andrew Ng Machine Learning course, he writes the initial optimization problem as follows. I am confused by the notation and suspect I am missing something simple. Given the ...
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10 views

Do you know about SVM plait?

I need to know about how I can applied many single SVMs? because I have read about SVM plait that does this kind of classifications that is using many single SVMs to improve the classification process ...
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1answer
40 views

R caret package - number of principal components when preprocessing using PCA

I am using the caret package in R for training of binary SVM classifiers. For reduction of features I am preprocessing with PCA using the built in feature [preProc=c("pca")] when calling train(). How ...
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72 views

How does Support Vector Machine compare to Logistic Regression?

Support Vector Machine (SVM) and logistic regression (LR) have been discussed widely in machine learning community, I know that both of them achieve pretty good performance. But, I am not sure how in ...
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22 views

SVR Overfitting?

My data has 3168x7 (being targets the first column). I´m trying to do SVR-RBF. I did 10fold , and gotta better results with gamma 8. But, when apllied to my external test set, I gotta bad results ...
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34 views

Understanding SVM and when to precompute the normal vector

I've been reading a lot about SVMs and have some questions about performing classification from the SVM model produced from a package like libSVM. From my understanding, for a linear SVM without the ...
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2answers
50 views

Using a gaussian kernel in SVM. How exactly is this then written as a dot product?

I am attempting to use SVMs for my class project. For this project, I have selected the gaussian kernel as, well, the kernel. That is, $$ k(\mathbf{x}_1, \mathbf{x}_n) = e^{-\gamma ||\mathbf{x}_1 - ...
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24 views

Are classifier hyperparameters selected within cross-validation or not?

I was reading this question about selecting hyper-parameters for a support vector machine classifier, where grid-search is presented as one option. Which one is correct, either ...
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1answer
47 views

Grid search for SVM parameters; is this is really how it is done?

Suppose I use nested 10-fold cross-validation with SVM. So, the inner-most loop will go around 100 times. Now, suppose I use a gaussian radial basis kernel function, which needs the parameter sigma. ...
2
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1answer
32 views

A priori decision for a linear vs RBF Kernel SVM

Still trying to understand the implementation of the linear vs RBF SVM. I get the RBF is used when the data is not linearly separable. My question is: Given a data set with a multiple class- is ...
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27 views

SVM One-vs-One vs One-vs-ALL SVM

For an unbalanced dataset annotated by human annotators in which each item is assigned to different classes, what is the argument for and against using any of One-vs-One vs One-vs-ALL SVM ...
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18 views

Comparing Two classification models using F1-score

I am trying to compare the results of 2 classifiers trained with SVM using the F1 score. Some papers that I have read and that do this have made me a bit confused. I have trained the 2 classifiers ...
2
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1answer
64 views

How to determine the optimal threshold for a classifier and generate ROC curve?

Let say we have a SVM classifier, how do we generate ROC curve? (Like theoretically) (because we are generate TPR and FPR with each of the threshold). And how do we determine the optimal threshold for ...
0
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1answer
61 views

How to know which feature mainly led to the prediction?

I have a classification problem where I use a model (say Logistic regression or SVM) to determine whether an instance belongs to class 0 or class 1. For a certain prediction on a test instance X, if ...
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27 views

My support vectors don't look correct

I am trying to classify a toy dataset using SVM. I only have two features and 20 instances. The decision boundary seems correct, however, the support vectors dont look correct. This is the relevant ...
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Pyramid Match Kernel: How to fit histogram grid around data points?

This question is directly related with Kernel methods and SVM, so I think this is a good place to ask it. I am planning to use Pyramid Match Kernel method for object recognition from depth images: I ...
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1answer
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How does the $\phi(x_i)$ function look for Gaussian RBF kernel?

I am trying to write programs for simple SVM cases. And what I am stuck at is that I am unable to find $\phi(x_i)$ functions for given kernels. For example there is Gaussian Radial Basis Function ...
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How does support vector clustering works?? [duplicate]

Please explain the working of support vector clustering in detail. I want to understand how SVM can do clustering. Thanks in advance
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68 views

How to do cross-validation when comparing different feature selection methods?

I am using SVM for a prediction task. My sample size is small, only N=140. Suppose I want to compare the prediction accuracy when using two different feature selection methods. Would it be better to: ...
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2answers
69 views

Does Support Vector Machine handle imbalanced Dataset?

Does SVM handles imbalanced dataset? Is that any parameters (like C, or misclassification cost) handling the imbalanced dataset?
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18 views

One Class SVM Training in LIBSVM

Could you kindly reply my queries in context of using LIBSVM for One class SVM. Assuming I have samples from one class only, do I need to put a lable for each sample...but a training file without ...
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14 views

LibSVM totalSV for multiclass

I am performing classification of K=9 classes using linear SVM with libSVM (MATLAB warp) I am using 400 samples of data to perform the training and I'm getting: totalSV: 203 I know libSVM uses ...
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22 views

Support Vector Machine with zero bias term

I'm looking for an algorithm to solve SVM with zero bias term. So dual form of such SVM is $max_\alpha \sum_i^n \alpha_i -1/2\sum_i^n \sum_j^ny_iy_jK(x_ix_j)\alpha_i\alpha_j$ subject to: $0 \leq ...
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10 views

SVM tends towards one class

I implemented a 1 vs all multi class SVM using libsvm. When I use the trained SVM to predict it will just assign all the samples to the class that is most abundant. Is there a chance that the data I'm ...
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20 views

The Shogun Machine Learning toolbox for SVM with precomputed kernel and zero bias

Can I use the Shogun Machine Learning toolbox for SVM with precomputed kernel and zero bias. I should be able to input pre-computed kernel and I also should be able to set bias zero.
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SVM with pre-computed kernel and zero bias

I have an optimization function, where I need to give my own kernel matrix and bias value is zero. The kernel matrix is calculated using the data but there is no specific formula for it. If I have a ...
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1answer
29 views

libsvm: scaling data results in less features?

I've scaled my training and testing data in BASH like so: ...