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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Which one is correct phase for neural network or support vector machine? Features or Inputs?

Which one is correct phase for Neural Network or Support Vector Machine? Features or Inputs? Based on ...
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What is the chance level accuracy in unbalanced classification problems?

Suppose one has a balanced classification problem (50% of 0's and 50% of 1's). In such a case, the so called chance-level accuracy of classifier would be 50%. What is the chance-level accuracy if the ...
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26 views

How to create a multidimensional data structure in R as input to kernlab's ksvm()

This is a revision/rephrasing of my question originally posted on stackoverflow. How should I create the training/input dataset for a ksvm model with multi-dimensional input data? The process for ...
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32 views

What are the best R packages for a classification problem with use of Neural networks [on hold]

Surfing on the internet shows me that there are a lot of different packages and functions which can be used to train neural networks via R. packages such as 'RSNNS', 'nnet','neuralnet', etc. I'm ...
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20 views

Clustering data with one feature

Is there any built in method to cluster data with one categorical dimension in R? Basically, I have a data set including week of the year and if an event happened in that week. I wanted to use ...
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20 views

Confounding factor in cross-validation

I have been exploring a dataset using support vector machines. I am solving a binary classification problem and using stratified K-fold cross-validation for performance estimation (the SVM ...
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15 views

Semi-supervised learning vs supervised learning, what are the benefits and limitations?

Just wondering if any previous work compared semi-supervised learning vs supervised learning? Currently, I have got both datasets with and without labeling. And therefore, it is intuitive for me to ...
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32 views

Help for interpreting SVM cross-validation results

I am using support vector machines for an unbalanced binary problem (0: 25%, 1: 75%). I do K-fold cross-validation with $K=10$. The metrics I get are: 80% classification accuracy on average for the ...
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39 views

Predict out of sample values for support vector machine

I wonder how I can use the results for svm() in R for out of sample predictions. Assume that we split the dataset into train and test sets and use ...
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13 views

Class weights in unbalanced SVM classification

The answer to this question says that class weights for unbalanced SVM classification can be picked so that that sums of the weights for each class are equal. Should this be done before ...
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Is it possible to apply SVM on one dimensional dataset in R? [closed]

Is it possible to apply SVM on one dimensional dataset in R? if yes, please suggest me the procedure.
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18 views

Cross-Validation in binary classification using only 10 positive samples (SVM)

I have a binary classification problem for which only $10$ positive samples are available for training. Negatives are in general in abundance, but I choose to use solely $70$ ($7$ negatives per one ...
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9 views

Using SVM on 1D data?

I'm working on a project that looks at extracting one feature and then builds up this process to extracting features of different types, etc. I'm wanting to display results throughout this process. ...
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plot does not show up for an svm object and no error is returned as well [migrated]

I am trying to use svm() to classify my data. A sample of my data is as follows: ...
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73 views
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Is an SVM's (maximum) likelihood uniquely defined as a function of hyperparameters?

I think that I must be reading this paragraph (below) incorrectly. Note that both types of evidence that we have defined in general depend on the inverse noise level $C$ and the kernel $K(x, ...
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17 views

Time-related & behavioral anomaly detection

Sorry in advance if my question seems a bit confused, I will try to be the most clear possible. I am in a situation where I analyse the behavior of target (cars, pedestrian) near a road. To do that, ...
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33 views

Which one is faster? MATLAB SVM or scikit SVM? [closed]

Which one is faster, SVM from MATLAB or SVM from scikitlearn?
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38 views

Support Vector Machines vs KNN

It was my understanding that in a separable case, SVMs produce the best separation possible and therefore will always produce the same or a better classification rate compared with say, 1NN, ...
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63 views

SVM kernel parameters value [duplicate]

I need a kernel for the following situation: 100 dimensions, 10 classes For every feature(in the features order) the maximum distance between any different pair of points is ...
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21 views

predict_proba is not available when probability=False

I' m trying to use scikit-learn for a classification, I get ...
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SVM-Light displays corrupted precision/recall results

I run SVM-Light classifier but the recall/precision row it outputs seem to be corrupted: ...
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hinge loss vs logistic loss advantages and disadvantages/limitations

Hinge loss can be defined using $\text{max}(0, 1-y_i\mathbf{w}^T\mathbf{x}_i)$ and the log loss can be defined as $\text{log}(1 + \exp(-y_i\mathbf{w}^T\mathbf{x}_i))$ I have the following questions: ...
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30 views

SVM output to probabilistic affiliation

How can I convert the svm output for multiple class classification(one vs one approach) to probabilistic values? Meaning that I want to have a probability for a tested element to be in each available ...
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1answer
31 views

A question about SVM kernels

this is a very basic question about SVM. I was using SVMs that are provided in the scikit for some problems, and noted that they are quite slow for big datasets. I then learned more about the ...
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18 views

Bounded and unbounded support vectors for nu-SVMs

How do we find out which of the support vectors for a nu-svm are its bounded or unbounded support vectors? For c-SVMs the test is easy: a support vector with $\alpha_i = C$ denotes a bounded support ...
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How many data points are required for support vector regression?

I have a trivial question which I could not find the answer out there. How many historical data points are needed for support vector regression? I know having a few data points (less than 10), the ...
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48 views

Find determining variables between two groups

I have a large dataset with many variables (for example: height, weight, color, category, revenue...) I am trying to compare two groups and find which variables determine the groups. My goal would be ...
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Support Vector Machines - building features out of word count and context

This python code creates features which reflect whether a given keyword is present or not in the given tweet. ...
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22 views

How Linear SVM works for Text Classification

I am working on text classification problem with Linear SVM. I have some basic knowledge on SVM. I am looking for information on how exactly SVM works for text classification problems, i.e. its ...
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Is it possible to compare the scores of different SVMs on the same test set?

Currently, I am training and employing an exemplar SVM with single positive image and several negative images. Then I am applying this SVM on a test set to get the SVM scores. For example, I have ...
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How to draw plot of the values of decision function of multi class svm versus another arbitrary values?

I am trying to draw a plot of the decision function ($f(x)=sign(wx+b)$ which can be obtain by fit$decision.values in R using the svm function of e1071 package) versus another arbitrary values. From ...
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13 views

How use F1 score in an unbalanced binary classification problem?

I have two trained models (MLP and SVM) that want check on unbalanced binary samples (out of sampl - True samples =3000, False samples = 200). I found that i can use ...
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27 views

Area under ROC curve vs. Accuracy in unbalanced sample

I have a binary classification problem with 3000 samples (number of 1 as outputs = 300, number of 0 as outputs = 1700). After balancing database (selecting 300 samples from 0 outputs) I trained the ...
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43 views

Machine learning framework for SVM, Random Forest

I need an library, or something that is already done for SVM and Random Forest algorithms. Can you give me some ideas? I don't have experience and I don't know what to choose. The restriction of my ...
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Why R linear ksvm don't use all support vectors

I was playing with svm and I made this data: x y | type ----------- 1 1 | 1 2 2 | 1 2 0 | 1 0 0 | -1 0 1 | -1 1 0 | -1 and using this setting ...
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Can Platt Scaling to calibrate probabilities be used for classifiers other than SVM?

I am using Gaussian Mixture Models as classifiers and I compute posterior probabilities from them for a 2 class problem. However, the probabilities are pushed towards 0 and 1 due to very skewed ...
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Find the relative importance of features weights in multi-class SVM without PCA - plotting coef distribution?

I'm classifing users with a multiclass svm (one-against-on), 3 classes. In binary, I would be able to plot the distribution of the weight of each feature in the hyperplan equation for different ...
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maximum mis-classification loss of hinge loss

In the plots and in some lecture notes, I read that hinge loss is bounded between (0,2). But I can not understand that. By definition, hinge loss is (standard one) ...
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68 views

Plotting learning curves for any classification algorithm

As recommended by Andrew Ng in his great course on machine learning, I would like to plot the learning curves for experiments I am running with Random Forest and SVM algorithms. The learning curves ...
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What's the probability that there exists a hyperplane that can split a dataset which have random feature values ?

Given n data points, each with d features, n/2 are labeled as -1, the other n/2 are labeled as 1. Each feature takes a value from [0,1] randomly (uniform distribution). What's the probability that ...
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Will adding additional features hurt the performance of SVM ?

Just wondering the effects of additional features. Following are several thoughts: If the additional features are noisy (can not distinguish the two classes), then additional features won't hurt ...
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Fat-tailed data and SVM

Does SVM perform poorly when fat-tailed data with outliers is used? What are some things that could be done to improve learning with such data? Does the choice of kernel and/or kernel parameter ...
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How much prediction accuracy of SVM (or other ML models) depend on the way features are encoded?

Suppose that for a given ML problem, we have a feature which car the person possesses. We can encode this information in one of the following ways: Assign an id to each of the car. Make a column ...
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Kernel SVM overfitting after training set extension

I am training Kernel SVM from sklearn package for binary classification problem. I perform a gridsearch for parameters optimization. Parameters are taken from following ranges: 'C':[1., 10., 100.], ...
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Trained Logistic Regression returns 'NAN' for some out of sample data

I'm using MATLAB R2015a, glmfit function for training and glmval for out of sample ...
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28 views

Is there any package in R that's commonly used for semi-supervised learning?

Is there any package in R that's commonly used for semi-supervised learning ? I have a dataset where I manually labeled 100 data points so I'd like to use semi-supervise learning for the rest of the ...
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33 views

Does the kernel trick really map 2d data to 3d data?

I want to learn something about kernel trick in svm, so I'm using this code: ...
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131 views

Confused about transposes in kernel notation

I am studying machine learning and I ran into a challenge that does not make sense to me. Maybe it is a crazy or a simple question. If we have kernel $ \phi_1(x)=[x, x^2]^T $ and $ \phi_2(x)=[2x, ...
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Probabilistic degree of confidence for the kernel SVM with RBF

Let $f\colon\Bbb{R}^n\to\Bbb{R}$ be the decision function of an SVM using the radial basis function (RBF), $$ k(\mathbf{x},\mathbf{x}')=\exp\Big(-\gamma\|\mathbf{x}-\mathbf{x}'\|^2\Big). $$ That is, ...
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What is test and what is training data in this SVM formula?

I am studying how to use Gaussian RBF kernels for mapping 2D data to 3D. In this link: Use Gaussian RBF kernel for mapping of 2D data to 3D, @MaxS provides an answer on this topic, but I can't ...