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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Hypothesis testing on SVM using parameters

1. Introduction I am working on a multi-class classification problem by using a ONE-VS-ALL classifier. I have no problem what so ever with the approach. I just wanted help in how can we interper ...
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LS-SVM time series forecasting

I'm trying to forecast a time series of air passengers using LSSVM with the help of the LS-SVMLab toolbox v1.8 from http://www.esat.kuleuven.be/sista/lssvmlab/, specifically the NARX model function. ...
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Classification and convex optimization of the cost function

From the literature I read that For a neural network, the cost function, J(W,b) is a non-convex function, gradient descent is susceptible to local optima; however, in practice gradient descent ...
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Does the confidence measure of a SVM have a meaning that can be compared across different models?

Suppose I train two SVMs ($m_1$ and $m_2$) on two different (potentially unrelated) problems. I then present datum $d_1$ to model $m_1$ and it outputs a vector $v_1$ of probabilities over the space ...
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Support Vector Machine: Output simulation from mathematical equations

If I have trained SVM model using the RBF kernel, how do i simulate the SVM output using the trained model and mathematical equations? How do I transform the test data? I am trying to write a code ...
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Validation of Support Vector Machine using sklearn

I have made a recording of two different sounds and I want to use an SVM in order to be able to distinct between the two. The process I have followed is: Divided each sound in multiple 20ms frames. ...
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What it mean by Training SVM

I am new to image processing. As my project I am doing "image classifier using SVM". I have the idea of my final software "I select some image and give it as input to my software and it will classify ...
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SVM file preparation [closed]

I have a file in which i have comma separated value like this 0.000,17.021,8.511,6.383,4.255,6.383,4.255,2.128,10.638,6.383,0.000,8.511,4.255,0.000,8.511,6.383,4.255,0.000,2.128,0.000 ...
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LSSVM Prediction using LS-SVMLab toolbox v1.8

I'm trying to forecast a time series of air passengers using LSSVM with the help of the LS-SVMLab toolbox v1.8 from http://www.esat.kuleuven.be/sista/lssvmlab/, specifically the NARX model function. ...
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26 views

What does make SVM a “soft computing” method?

Soft computing is defined in [1] by the capability of "operating with uncertain, imprecise and incomplete information in a manner that reflects human thinking". So, based on my limited understanding, ...
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AdaBoost - How to use the distribution D [closed]

I am trying to implement AdaBoost algorithm in Python. I have m weak classifiers in list called classifiers. I have vector _D ...
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40 views

Confused Scikit results

I am doing classification machine learning on a particular dataset on which an SVM model (using Scikit.learn) is giving a Matthew's correlation coefficient (MCC) of ...
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Weights and hinge loss/linear SVM

Say I have a dataset with this distribution Class A: 10 Examples Class B: 100 Examples Class C: 1000 Examples Hence, i am trying to build a classifier using linear SVM. Baring all concerns about ...
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42 views

How to boost the performance of support vector machine?

I have 4 different data samples: Stage 1: [152 X 27578] Stage 2: [48 X 27578] Stage 3: [48 X 27578] Cancer: [63 X 27578] Each sample are the different stage of cancer in descending order. Here I ...
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1answer
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What is the purpose of +1/-1 constraint on SVMs

It is common knowledge that all SVMS try to optimize some permutation of the following equation with the provided constraints: however, my question lies on the purpose of the constraint >= 1 and ...
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Automatic mail classification

I'm building a mail classifier in Python 3. I've successfully built classifier to classify spam/ham using SVM (LinearSVC to be precise) using scikit-learn. But the next challenge is to auto bucket the ...
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3answers
33 views

How to perform grid search effectively for tuning SVM parameters in cross validation?

I have C and gamma parameters for RBF kernel to perform SVM classification through cross validation in R software. How to fix values for grid search to tune C and gamma parameters? For example I took ...
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Junk data in classification

I have a data set which I would like to classify it into two classes. And I have training data for each of those classes. However there is also junk data which I also have some samples but since it's ...
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alternatives for OC-SVM for one class unsupervised classification in R

I have been working with OC-SVM for one class unsupervised classification using R. I would like to try other techniques for the same purpose. However, I find lots of outlier detection techniques, like ...
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handle unbalanced data in multi-class

I have three classes A,B,C. They are different in their feature values. Another class D is the one I want to distinguish from A,B,C. From my perspective, I can treat A,B,C as one class (let's call it ...
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32 views

SVM classifier - can I average multiple models?

I'm performing SVM classification on a relatively large data set (~1M rows, 4 variables). I want to assign a classification score to each row, not evaluate input parameters, so following the top ...
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Intuition behind Latent SVM

The relevant paper (I think) is : Felzenszwalb, Pedro F., et al. "Object detection with discriminatively trained part-based models." Pattern Analysis and Machine Intelligence, IEEE Transactions on ...
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33 views

k-Means as a preparation phase for supervised learning

I am working on a supervised learning project and I am planning using the $k$-means algorithm to generate clusters, i.e. labels, on a continuous variable in order to apply a Classification SVM. ...
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63 views

Why use logistic regression instead of SVM?

I have a very basic question, but it's not really clear to me. When would one choose logistic regression over SVM? Maximum margin property seems more justifiable then whatever log. reg does. Is it ...
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linear kernel SVM

The linear kernel is defined as: $K(x1,x2)=\langle x1,x2\rangle$. I can see that all that this kernel does is to calculate the dot product in the original space of the data. Why is this kernel then ...
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58 views

When to use regression trees/forests?

As I was looking for a fine regression algorithm for my problem. I found out one can do that with simple decision trees as well, which is usually used for classification. The output would be something ...
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49 views

SVM Vs Neural Network Vs Random Forest classifier comparison on multi class problem

Any idea if SVM or Neural Net or Random Forest works better on a classification problem on the same multi class dataset? I mean, in general, which should outperform the comparison?
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SVM - non separable case/soft margin, where are the support vectors

My Question is - in the inseparable case(where we add slack variables) $\begin{equation*} \begin{aligned} & \underset{w}{\text{minimize}} & & \ ||w||^2 + C \sum_{1}^{m} \epsilon_i\\ ...
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262 views

Are there applications where SVM is still superior?

SVM algorithm is quite old - it was developed 1960s, but was extremely popular in 1990s and 2000s. It is a classical (and quite beautiful) part of machine learning courses. Today it seems that in ...
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Why SVR kernels other than 'linear' don't work for this toy dataset!

I'm a little new with modeling techniques and I'm trying to compare SVR and Linear Regression. I've used f(x) = 5x+10 linear function to generate training and test data set. Here we've discussed why ...
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32 views

Mercer's condition

I am having a very hard time understanding Mercer kernel. If any sequence of data points $x_1, ... , x_n \in R^d$ and coefficients $c_1, ... , c_n \in R$, satisifies the inequality $\sum^n_{i=1} ...
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Feature selection using RFE in SVM kernel (other than linear eg rbf, poly etc)

At this link, there is an example of finding feature ranking using RFE in SVM linear kernel. If I want to check feature ranking in other SVM kernel (eg. rbf, poly etc).How to do it? I have changed ...
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Is it acceptable to use class probabilities as weights for a weighted average when the bins are numbers 1 to 5?

I have a Multi Class SVM that can predict what class some observation belongs to. There are 5 classes. They are trained for observation that scored 1 to 5. I want the MC-SVM to predict a class for ...
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SVM - relevance of linear kernel

The linear kernel is of the form K(x1,x2)=$<x1,x2>$. I understand that kernel functions help us compute a dot product in some high dimensional space. In the case of the linear kernel, I see that ...
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Classifier performance difference

I am using SVM and Random forest for classification purpose on a dataset. I am able to optimise the SVM parameters and SVM is providing very good performance in terms of accuracy, recall. But,at the ...
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Single layer NeuralNetwork with RelU activation equal to SVM?

Suppose I have a simple single layer neural network, with n inputs and a single output (binary classification task). If I set the activation function in the output node as a sigmoid function- then the ...
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measure the convergence rate of SVM under cross-validation

I want to measure the convergence rate of my SVM based classifier, i,e how many data points are necessary to build an accurate classifier. I have an initial learning set from which I remove X% (X from ...
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37 views

SVM accuracy after log transformation

I am using SVM (RBF kernel, the LibSVM implementation) to deal with a classification problem. When I use a log 10 transformation for may features values instead of using the default scaling method ...
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1answer
86 views

Data Visualization after k-fold Cross Validation step

I need suggestion about data visualisation for cross validated data. I want to plot data after cross validation and need suggestions how to do that? I am thinking to plot like this If I use a ...
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Why the constraint $\xi \geq 0$ can be dropped from l2-svm?

Why the constraint $\xi \geq 0$ can be dropped from l2-svm? I have found a solution in the following notes: https://see.stanford.edu/materials/aimlcs229/ps2_solution.pdf . However, I do not quite ...
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Music Genre Classification using combined SVM and HMM

We are doing a Music Genre Classification software using C#. We want to know if combining HMM and SVM as a classifier, by using the distance of trained data on the hyperplane as an input sequence for ...
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optimise parameter in svm in R using GA

Hello Can anybody help me to code predict svm model target value called RR with svm and cross-validation k-fold using GA optimization method?
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Is validation set important in SVM regression analysis

I am new to machine learning and currently reading a paper about a ANN modelling in which they have divided their dataset into Training , validation and testing set. I have carried out some minor SVM ...
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Clustering + SVM -> transductive SVM?

Given a binarily labeled train set, and an unlabeled test set, consider the following two-step classification system: step 1: the train and test data is clustered. step 2: an SVM is fitted for each ...
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43 views

K-fold in grid-search for linear svm C parameter giving same value?

So every time I do a GridSearchCV with KFold, stratified or not, I get the same accuracy score and STDev for values of C=1,C=10, and C=100. I then did a special ...
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Remove features with high correlation

In a classification problem using Linear SVM, I am trying to remove variables which have a strong correlation (Pearson) between them from a dataset. What is the usual threshold recommended? I ...
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How much does adding few input features to a SVM model affect its 'optimal' parameters?

I have trained a SVM model with RBF kernel, the parameter values of which have been selected by grid searching a wide range of (cost, ...
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Use of Random Forests for variable importance as preprocess before another analysis

the question Demonstrate the speed and accuracy of properly applied 'Random Forest' as a variable importance selection tool especially in handling very large data against alternative approaches such ...
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Using ML approaches to build a recommender engine for sales team

I work at a startup as a developer, but I wanted to help out our sales team with running some ML algorithms on the data. A bit of context: Most of our revenue comes from ad purchases, so in a ...
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Permutation test for ML model

I would like to do some basic check that my huge supervised ML model is not over-fitting (two-class problem). I was thinking about the following: Randomly permute labels using entire dataset Re-do ...