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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18 views

How do we come up with the SVM Kernel giving $n+d\choose d$ feature space?

I was going through the CS229 notes on SVM and Kernel tricks and I came across the following line. More generally the kernel $K(x,z)=(xTz+c)^d$ corresponds to a feature mapping to an $n+d\choose d$ ...
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What's the best strategy to fill NAs for a predictor in supervised learning e.g. SVM?

What's the best strategy to fill NAs for a predictor in supervised learning e.g. SVM? I have monthly data for all other predictors since 1963 and for one predictor I have data since 1990 only. So I ...
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12 views

Training/test splits (Monte Carlo sensitivity analysis) or Cross-validation

I am using SVM in Matlab (fitcsvm function) to train a classifier for a problem with two classes. Further, I have three features, e.g. A1, A2 and A3, available for each observation composing my full ...
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SVM classification metrics are all 1 although there are mistakes in classification

Here is a fitted LinearSVC model showing the learned separating hyperplane for my training samples: And when I use ...
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24 views

How to determine equation of hyperplane for SVM?

Assume we have only two features in our training dataset that is already classified into class C1 and class C2. The transposes of the feature vectors are given below for each class: C1: [2 6], [1 1], [...
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13 views

SVM predicts always the same class

I have a dataset with tf-idf values and their corresponding classes and I am trying to do predictions using SVM. The problem is that all the results that it produces have the same class. Most related ...
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21 views

Testing for causality with Support Vector Machines

Can a support vector machine (SVM) be used to test for causality between 2 or more variables? I know that the original purpose for SVM is classification. I also know that there is a variation of the ...
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28 views

Hard-margin SVM and logistic regression for non-linearly separable data

Hard-margin SVM doesn't seem to work on non-linearly separable data. It seems to only work if your data is linearly separable. What happens if you try to use hard-margin SVM? Does the algorithm blow-...
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How can I extract the correct hyper-plane from sklearn.svm's LinearSVC

I'm not certain I understand how sklearn's Linear SVC works. I had assumed that it would find an optimal hyper-plane to divide one class from another. I tried to recover the separating hyper-plane ...
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18 views

SVR with combination of kernels

I am a beginner, and I am looking for some advice regarding the use of Support Vector Regression (SVR) to model (or fit if you prefer) a trend. Before you suggest other methods, for a number of ...
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26 views

SVM formula variations

I was reading about SVM in Wikipedia and saw that the formula was written like this (with minus): $$ \vec{w} \cdot \vec{x} - b = 0 $$ While other sources have it like this (with plus): $$ \boldsymbol {...
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sklearn's SVM classification failure

I'm trying to fit a trivial classifier but I'm not sure what am I doing wrong. I'm providing scikit-learn's svm.SVC linear classifier with two samples of X=[[0.], [0.5]] and labels y=[0, 1] and I get ...
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Does it make sense train Linear Support Vector Regression with an epsilon of 0?

In the sklearn.svm.LinearSVR implementation, the default parameter of epsilon is 0.0. And the documentation says "if unsure,...
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43 views

Relationship between structural or statistical properties and hardness of classification

I am trying to understand the relationship between structural or statistical properties of training dataset and hardness of classification in the context of binary classification with SVM using RBF ...
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16 views

Finding meaningful boundaries between two continuous variables in R

To find the relationship between two columns of the iris dataset, I am performing kruskal.test and p.value shows a meaningful relationship between these two columns. ...
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How to define a decision boundary between confidence regions in a redundancy analysis?

I did a redundancy analysis (RDA) and got two groups. The figure shows the ordination diagram in the {RDA1, PC1} coordinates, on which the centroids coordinates and the 95% confidence regions were ...
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What is $\alpha$ in SVM Lagrangian fucntions

Is it a column matrix or a row matrix? I saw on a CMU slide that $\alpha$ is defined as $\alpha=(\alpha_1, \alpha_2, ..., \alpha_n)^T>=0$? I dont see much of a formal definition for the exact form ...
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Convert the following expression w.r.t to the whole dataset instead of element of the dataset?

I am in the process of expressing the w in LSSVM with data points and constants. After I resolve the KKT conditions for the LSSVM I got $$w = \sum ^N _{i=1} \alpha_i x_i$$ Is it possible to convert ...
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31 views

Is the concept of Kernel in Linear Algebra and kernel for SVD the same?

Is the term kernel used in Sklearn to execute the SVD machine learning algorithm conceptually related to the notion of a kernel in linear algebra ( null space )? Or do they happen to use this same ...
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60 views

explain the cost function of scalar vector machine (SVM) [duplicate]

I am unable to understand the above cost function. The two possible outputs ($y$) are $-1$ and $+1$. As far as i know, $x_i$ values are individual training example values, $y_i$ are actual true values ...
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38 views

Why do distances to hyperplane increase with more training samples in a One-Class SVM?

I am using a One-Class SVM from for anomaly detection. I observe that the distance of classified samples increases roughly proportional with the number of training samples. This is true for inliers ...
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64 views

Can Support Vector Regression be used with count data?

Can Support Vector Regression be used with count data? If it can be used for count data, can you give me information about it? UPDATE: I mean something like a Poisson GLM but using SVR
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Selecting Correct Statistical Methodology

I am designing a predictive model, and I am unsure of how to group the data from individuals companies, or whether I even need to group that data, and it's valid to treat each row as an independent ...
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Which statistics can be used for between group classification with searchlight (fMRI)?

I am using SearchLight from the Nilearn module to analyse my fMRI data. I have two groups of participants and use a searchlight based on linear SVM with 5-fold CV to classify the data into those two ...
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Handling Features with Outliers in Classification

Let's consider I have a data set of student details. Age would be a typical feature in such a data set. Just because there are typically fewer people aged above 40 in such a data set, which is ...
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Is it correct to decrease the alpha parameter in sklearn SVM pipeline to improve performance?

I'm trying to find the best parameters for a Fine-grained Sentiment Analysis of a dataset of movie reviews. This is the current code: ...
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Is it an overfitting problem for SVM classification?

I am new in Machine learning, and I want to detect emotions from the face. Preprocessing: I used equalizeHist to equalizes the histogram of grayscale images (JAFFE database with 213 images), in the ...
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51 views

Extremely hard binary classification problem [closed]

Could anyone point me to a collection of binary classification dataset where a support vector machine algorithm will miserably fail?
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20 views

Getting feature weights with permutation_test_score()

I am using sklearn to fit a SVM to some data. Since I wanted to use cross-validation and evaluate my classification accuracy using permutations, I am using the permutation_test_score() function (https:...
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1answer
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Feature Scaling for Targeted Proteomics with Internal Normalization

Okay, I have spent a whole day now looking for an answer. I think I have a unique case when it comes to scaling and SVM. Briefly, the data attached is proteomic data collected from mass spectrometry. ...
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23 views

Feature map of Polynomial Kernel

The polynomial kernel is defined as $k(x,x') = (\langle x,x' \rangle +c)^m $ The feature map for polynomial kernel as introduced by my lecturer is given as $\phi:x \mapsto c_i(x_1^{i_1}+x_2^{i_2}+x_3^{...
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32 views

Support Vectors

My professor gave me this definition All vectors $x_i$ with $y_i.d(x_i,H(w^*,b^*))\leq Margin^*$ (which means that lying inside the tube) is called as support vectors. Does this mean points within the ...
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19 views

How do the decision boundaries of logistic regression and support vector machines compare?

I'm not so much interested in implementation details, just how the decision boundaries of the trained models would compare. The answer depends on regularization, kernel used and so on, so lets say ...
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27 views

SVM for fMRI: unbalanced data - leave-one-triplet-out?

I am using SVM (sklearn.svm) to classify fMRI data from two groups of people. One group has n = 25 and the other n = 26. Everyone except for one person has seen 96 trials, so in total I have 2,496 ...
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39 views

Why do neural networks outperform SVMs on image recognition if SVMs have the less generalization error?

Why do neural networks outperform SVMs if SVMs have the less generalization error according to Vapnik? Is generalization error only useful in data scarce environments? Is it because neural networks ...
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27 views

Does SVM suffer from curse of high dimensionality? If no, Why?

While I know that some of the classification techniques such as k-nearest neighbour classifier suffer from the curse of high dimensionality, I wonder does the same apply to the support vector machines ...
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Why are 'Mean squared error' or 'Squared correlation coefficient' only calculated for Epsilon SVR and Nu SVR?

Why does libsvm only calculate 'Mean squared error' or 'Squared correlation coefficient' the for SVM types of Epsilon-SVR and nu-SVR? What is the reason these aren't appropriate for C-SVC or nu-SVC? ...
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20 views

Can the coefficients of a linear kernel SVM be used to validate the feature importances of a decision tree?

I'm currently using sklearn to fit a decision tree to a small data set (210 rows x 180 cols). Interpretability is key as we'd like to be able to use the DT to make a white box model in order to tune ...
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18 views

Maximal Margin Classifier (Optimal Separating Hyperplane) Overfitting when dimension is too large?

In "Introduction to Statistical Learning", for maximal margin classifiers, they say: "Although the maximal margin classifier is often successful, it can also lead to overfitting when $p$...
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10 views

Adaptive Sampling techniques for SVM?

I am an Engineer interested in creating a surrogate model of a certain phenomenon in the context of reliability engineering. Essentially my quantity of interest is the Limit state function/stability ...
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43 views

Confusion between basis functions and SVM feature mapping

I'm new to Machine Learning. I have just read an article about basis functions. https://www.cs.princeton.edu/courses/archive/fall18/cos324/files/basis-functions.pdf Apparently, the basis functions ...
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Plot SVM boundary *after* training in Python

I want to train SVM on multiple features for high accuracy. Then I want to do the visualization: reduce dimensionality to 2D (with PCA, t-SNE or anything else) and plot the learned decision boundary. ...
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24 views

Multivariate regression or SVM

I have a group of independent variables (size: # of subjects x 4) and a set of dependent variables (size : # of subjects X 100). I am hoping to figure out if there is a way to decompose both ...
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23 views

does one class SVM (SVDD) detect Novelty or Outlier and what are the supporting vectors of SVDD

From my understanding of one class SVM (SVDD), the training data should all be the normal points (don't include outliers). Then a new point is added, we can use the ...
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Dimensionality problem in dual SVM regression formulation

Consider the Boston Housing dataset. If we denote the house price with $y$ and the vector of predicting variables with $x$, then the Kernel SVMs are solved by considering the following dual convex ...
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1answer
64 views

why is rbf kernel svm a non-parametric algorithm?

I was reading up the difference between parametric and non-parametric models on this site: https://sebastianraschka.com/faq/docs/parametric_vs_nonparametric.html It says that linear SVM is parametric ...
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Choose proper test for two numerical vectors in different length

I am currently struggling to find the most proper test to serve my needs. The thing is, I have one data matrix. The columns are computational features and each row corresponds to a patient. The last ...
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How is a polynomial kernel with infinite degree different from RBF Kernel?

I was reading about polynomial and RBF Kernels. According to my understanding: Polynomial kernels with degree >1 map the non-linear data into a higher dimensional feature space. Data that aren't ...
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Prove that the mixed partial derivative of a valid kernel is still a valid kernel

I have a vague memory of reading somewhere that the mixed partial derivative of a valid kernel is still a valid kernel but I cannot seem to find the original source. Does anyone have anything on it?
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Will non-linear data always become linear in high dimension?

I was reading the Hands on ML book and I'm on the SVM and Logistic Regression chapters. I started looking up more on these algorithms and apparently they are "linear" classifiers i.e the ...

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