Questions tagged [svm]

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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Weights from RBF Support Vector Regression

I was doing some research on Support Vector Regressions with Radial Kernels and I have consistently come across that the weights from the RBF cannot be calculated implicitly because of the ...
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SVR feature selection

I'm trying to go through feature selection with SVR (trough caret package in R). Working on a dataset with 400+ points and 20+ features and 2 target variable. Can I use correlation coefficient for ...
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What is median F1 score in supervised machine learning?

I have 20 labelled class in my dataset. I trained my data by SVM machine learning model based on binary(one vs all) classification technique. Then evaluate F1 score for each binary model. But I need ...
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Is it a good idea to implement a sklearn model for a real time image processing application?

I'm testing a support vector machine (SVM) model trained with scikit learn library for image processing, but i don't know exactly if for real time this library could be better than tensorflow or both ...
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I want to know the relationship between Discriminant functions and the kernel in SVM

The following articles are reprinte of #3338212 of math.stackexchange.com. It was recommended to ask this community at math.stackexchange.com. The following 【Quiz】 and 【Official Answer】are the ...
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Distance between 2 hyperplanes in SVM formulation

During the SVM formulation, the 2 hyperplanes is given by the equations: wᵀx + b = 1 ---------(1) wᵀx + b = -1 ---------(2) Now, the margin between these 2 hyperplanes is given by: 2/||w|| ...
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Support Vector Machine: identifying support vectors and kernel linear separability

I went through the MIT Artificial Intelligence lecture on Support Vector Machines by Professor Patrick Winston: https://www.youtube.com/watch?v=_PwhiWxHK8o I've got a couple of questions. Would be ...
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SVM as linear equations

I'm using SVM for a regression problem (sklearn.svm.SVR). After I train my model I use these 2 attributes svr.coef_ and ...
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Understanding the reproducing property of RKHS

I am currently trying to learn about Reproducing Kernel Hilbert spaces (RKHS) and would like to gain some intuition about its reproducing property. The RKHS is defined with kernel $k(x,x')$ which maps ...
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Testing a One-Class SVM in python

I have 525 negative images and 25 positive images that I want to detect. Using GridSearch, I can define precision/recall on the negative samples + 12/25 positive samples. My plan being to keep 13/25 ...
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class_weight = 'balanced' if GridSearch on unbalanced data set?

I'm trying to optimize the hyperparameters of an SVM. I have an unbalanced data set with more than two classes. In some classes very many samples are included in others very few. Using GridSearchCV, I ...
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Does increasing the margin value/ delta in a SVM loss function decrease the frequency of coming across kinks when evaluating the gradient?

According to http://cs231n.github.io/optimization-1/, kinks refer to non-differentiable points of a function. Even if the analytical gradient would be zero at such a point, the numerical gradient ...
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Calculating Accuracy in Binary classification Using SVM

I am a beginner; studying SVM with OpenCv and Python I am bit confuse about SVM and SVC; when I searched and find out " SVC is the support vector machine algorithm fot the multiclass problem and ...
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why the result of primal sim is not the same as linear kernel?

I have a data set with 36000 rows and 9 columns. so n<< m. this is multi classification SVM. I solved this by primal model in OSQP. then I used R package e710 and svm with linear kernel. the ...
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mathematical simulation of svm workings

i cant seem to work my head around the mathematical part of svm i understand the concept and the derivations but the the part that comes after lagrangian formulation is where im stuck..(googling didn'...
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Why does feature scaling improves accuracy? [duplicate]

With feature scaling we just change representation of the data. This can make our model run faster but how this can improve accuracy? It is the same data after all. When I train my SVM without ...
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What are kernels in support vector machine? [duplicate]

What are kernels in support vector machines? I have tried many contents but i am not familiar with Lagrange and Laplace concept in mathematics. So anyone can please elaborate concepts of kernels in ...
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Training data for extracted license plates from car images

I am working on a project which uses machine learning and image processing techniques to detect/extract license plates of a vehicle given an image. In my module for data preparation and feature ...
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Random fourier features and Bochner's Theorem

The paper, Random Fourier Features for Large-Scale Kernel Machines by Ali Rahimi and Ben Recht , makes use of Bochner's theorem which says that the Fourier transform $p(w) $ of shift-invariant ...
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Why SVM performs better than logistic regression for well separated case?

In ISLR page 357, the author mentioned that " When the classes are well separated, SVMs tend to behave better than logistic regression; in more overlapping regimes, logistic regression is often ...
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is a SVM model non non-reasonable if we don't have soft margin on it?

do all svm models have soft margin? how about if I don't put it in my model? from academic point of view , is it so unexeptable? I don't want to put epsilon on my constraints because I want to solve ...
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How would phi of the gaussian rbf kernel map a 100-by-3 dimensional feature matrix?

Would a 100-by-3 dimensional feature matrix be mapped into a 100 dimensional or into a infinite dimensional feature space, if the mapping would not be bypassed by the Gaussian RBF Kernel? Following ...
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SVM derivation - using different classes

in every book I considered (like The Elements of Statictical Learning or An Introduction to Statistical Learning) the derivation of the SVM is always done with classes $y_i \in \{-1,1 \}$. In my ...
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SVM: achieving ROC curve by varying misclassification costs

Assume that we have SVM model for binary classification with objective function as follows: $$ min(\frac{1}{2}\omega\cdot\omega +C^{+}\sum_{i|y_{i}=+1}^{n}\xi_{i}\quad+C^{-}\sum_{i|y_{i}=-1}^{n}\xi_{i}...
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Tuning SVM parameters in R

I am training an SVM model for the classification of the variable V19 within my dataset. I have done a pre-processing of the data, in particular I have used MICE to impute some missing data. Anyway a ...
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how to use RFB for primal svam model?

I know if we want to solve primal model of non-linear SVM, we have to generate new features. for example for kernel (1+xz)^2 for primal problem for any pair of features x1 and x2 we have to generate: ...
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Can we use SVM and Random forest for classification of one-dimensional data?

I am working on flood inundation mapping using remote sensing data. I am using a single band, a simple threshold value can be used to separate land and water. I am interested in knowing, can we use ...
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non-linear SVM for primal problem

is there any method to do non-linear SVM with solving primal problem? I mean not using lagragian-multiplier . in this case we can not use kernel trick, so what exactly we should do?
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SVM classification reliability with little data

I'm trying to train an (RBF) SVM model to get a binary classification (1 = class, 0 = no class) based on some features. My dataset is quite small: I have 2500 records for training and 300 for tests. ...
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Why a weight vector can be expressed as a linear combination of the training examples?

I'm digging into SVM's, and there is a certain step which is not all clear to me, and it is the part of representing $W$ as a linear combination of the training examples. How can we suppose that this ...
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C penalty in SVM - larger C increases the margin or reduces the margin?

I get contradictory information on what the penalty value C does in SVM. page 346,347 of the following book says, larger C means larger misclassification is allowed and margin will be larger. http:/...
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Similarity of perceptron criterion and SVM

In the book "Neural Networks and Deep Learning" by Aggarwal there is an exercise 2.10.1: Consider the following loss function for training pair $(\overline{X},y)$: $$L=max(0, a -y(\overline{W} \...
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Will applying SVM in high dimensions (7) with limited training examples (41) likely lead to overfitting?

Right now I have a dataset with 41 training samples (and no testing samples either unfortunately). There are 7 features, but I've been treating the problem as a 2-D problem thus far (in other words, ...
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SVM's 10-fold cross validation is not enough?

I am working with SVM upon a dataset having 138 subjects and 34 features. My question is that when we tune the SVM by default it uses 10-fold cross validation, so, do i need to split the dataset again ...
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Dealing with imbalanced dataset without using Undersampling or Oversampling

Let's say I have a dataset with 100,000 class A training observations and 400 class B training observations. I want to use Support vector machine for this binary classification problem. Instead of ...
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Is consistency among classifiers a good measure for feature sets?

I have two feature extraction approaches (or feature sets) to describe the same data, the feature set 1 has overall consistent results among 4 different classifiers (SVM, Logistic Regression, Adaboost ...
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How to set the parameters of SVM with polynomial kernel?

I learned there two parameters of the polynomial kernel: the intercept and the degree of the polynomial. The question is, how do we decide the values of these parameters? From where I should start ...
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How to Interpret output Coefficients of Linear Support Vector Regression?

I'm looking to interpret the output from my SVR model. I know that with SVM you can't directly interpret the coefficients of the model but that you first have to take a dot product With that said, ...
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Can data ever be too high dimensional for the Lasso?

I'm trying to implement Lasso on high dimensional textual data. Format of Data: p ~= 45,000, n~=4,000 When running the Lasso, I get a training score of 0 and the number of features selected as 0. ...
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Why is the SVM in canonical representation necessary?

For my exam preparation I have following question from an old exam: I know that the SVM in canonical representation is $$\min_{i=1,...,n} |\langle \omega,x_i \rangle + b | = 1$$ So why is it ...
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Linear separability

What algorithm should be used to find the plane for a linearly separable dataset? I know that this is a quadratic programming problem, but I can’t find a suitable algorithm, could you please help? ...
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Is kernalized linear regression parametric or nonparametric?

We know that for linear regression, we can predict: $$\hat{y} = w^Tx +b$$ Where $w$ is the parameter that minimizes the square loss. It is easy to prove that for the final solution using gradient ...
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A derivation regarding kernel regression for the support vector machine

THis is from the Elements of Statistical Learning book page 437 in the section of support vector machine. Can anyone give me some hint for the missing derivation steps for why 12.49 is true (as seen ...
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Expansion of inner product for polynomial kernel for SVMs

On page 424 in "The Elements of Statistical Learning" by Hastie et al (2013) (https://web.stanford.edu/~hastie/Papers/ESLII.pdf), we see the following expansion of a polynomial kernel with degree 2: ...
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why in SVM we have different indices for dot product?

I am confused by Lagrangian method in SVM, I can not understand why we use different indices in dot product. Suppose with using Lagrangian W is : $ W_{i}=\sum_{i}L_{i}y_{i}x_{i} $ In SVM ...
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Proving that an SVM problem with a complex loss function is convex [duplicate]

The end goal, along with proving that the problem is convex, is to be able to get the problem into a form that can be coded in CVX. I have m positively labeled data points $x_i$ $\in$ $\mathbb{R}^n, ...
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Proving that an SVM problem with a complex loss function is convex

The end goal, along with proving that the problem is convex, is to be able to get the problem into a form that can be coded in CVX. I have m positively labeled data points $x_i$ $\in$ $\mathbb{R}^n, ...
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Weird precision recall curve on SVM and RandomForests

I am comparing the performance of an SVM and a Random Forest. The Random Forest gives me: The SVM output is a bit bizzare And both get completely different when I take a sneak peek in the test set ...
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Which ML Algorithms are affected by dummy variable trap?

My understanding is that regression models are affected by the dummy variable trap. What about other machine learning algorithms e.g. linear svm, logistic regression? Also, if an algorithm is not ...
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Is it OK to have only a single class labels in test data for prediction with one-class-svm?

I have a data which has only a single class, namely, '0'. There is no 'not 0' class. The one-class SVM model was trained on a <...