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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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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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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Uniqueness of SVM solutions in term of l1 and l2 losses

I have been reading several articles to find reasonable answers for the difference between L1 (hinge loss)and L2 (squared hinge loss) in solving the primal and dual SVM problems. I need help to find ...
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one class SVM modelling

I have some doubts about how to model a system based on one class SVM, which I plan to use for detecting outliers or anomalous data. For example, when I used a neural network or SVM model the ...
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what would be a recommended division of train and test data for one class SVM?

I have checked the following one class SVM classification in R that was posted in this thread: https://stackoverflow.com/questions/27375517/one-class-classification-with-svm-in-r In this program the ...
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OneClass SVM does not detect a single outlier

I am working with system metrics (shown in figure) which are a collection of time series of length 15 minutes. The resolution is 10 seconds per point, so I have exactly 90 data points per time series. ...
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Definition of a support vector (SVM)

I have a question regarding the definition of support vectors. It is usually stated that support vectors are those vectors which lie on the hyperplanes and hence define them. But how is the green ...
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Complexity comparison of XGBoost, Logistic Regression and SVM

Suppose that for a multi-class classification problem I am getting the same performance from these three classifiers. From the complexity perspective, which one should I choose (i.e. in terms of their ...
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Root Mean Square error or Standard Deviation to focus for Machine Learning model selection?

I have used Linear Regression and Support Vector Machine regressor model to predict the dependent variable. In Linear regression prediction the Root Mean Square error is more but standard deviation is ...
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Support vectors and functional margin = 1

In http://cs229.stanford.edu/notes/cs229-notes3.pdf, it states that training samples that have functional margin exactly equal to one, then they are the support vectors. The ones that have functional ...
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Tuning parameters of SVM in tune function

I would like to fit a radial SVM onto the data. The tuning parameters cost and gamma are chosen by CV with the tune function. However the summary of the optimal model does not show the optimal gamma ...
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Assumptions for machine learning algorithms

Every machine learning assumes that data has some characteristics like in linear regression, the features should not correlated and the variance in the error is constant, etc. Is there a reference ...
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What is the difference between kernel function and kernel trick?

My question is regarding the SVM topic. What is the difference between kernel function and kernel trick? Are they same and refer to the same thing?
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One-Class SVM for Unsupervised Anomaly Detection

I'm trying to implement one-class SVM for my unsupervised data. I need to mark the data as 1 if the values are normal and -1 if the values are anomalous. I'm trying to implement this algorithm with ...
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Workflow to develop SVM to predict behavioral performance from neural data

I'm quite new to this and wondering if you can help. I'm looking to create an SVM to determine whether neural data from several electrodes can predict behavioral performance on a task. Does the ...
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how to set the hyperparameters ranges when hyperparameters optimization?

I am using machine learning algorithms to solve my problem. I do hyperparameters optimization in my training data. I am confused that how to set the hyperparameters ranges or the guide principle. For ...
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heuristics for gamma in rbf kernel

My question is a follow-up to this question: SVM rbf kernel - heuristic method for estimating gamma. Basically, I want to find interesting values for gamma by first calculating the pairwise distance ...
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Kernelized linear regression

In kernelized linear regression, we find out that our weight vector $w$: $$ w = \phi^{T}\alpha = \phi^{T}(K+ \lambda I)^{-1})y $$ Here, $K$ is the kernel matrix and $y$ label of the training instances ...
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What's the score employed by Platt scaling to compute SVM posterior probabilities?

I have read the Platt scaling approach to compute posterior probabilities for the SVM classifier $P(y=1|x)$. In Scikit-learn's SVC (SVM) implementation this is the approach used to produce ...
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ROC AUC of 0.5 on train set

I am trying to build a binary classifie, but my classifier does not seem to learn anything from my data (I get AUC of 0.5 when I try and predict the train set - most of my observations are predicted ...
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What could be an intutive understanding of a hyperplane?

This "Hyperplane" word gradually becoming more important to understand as I delve deeper into machine learning applications. To explain Hyperplane, the wiki article majorly speaks about one ...
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Apply panel data techniques to a panel where observation can be assumed to be independent?

I'm working with a data-set and am unsure if I should apply specific panel data techniques to it or not. The data consists of panel data for municipalities in the Philippines and damage cause to rice ...
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Removing training examples from SVM [closed]

If we have a SVM that already classifies a training set. Is it possible to remove examples from the training set and still produce the same SVM?
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Pros and cons of different MKL algorithms

I have been using multiple kernel learning (MKL) to train a classifier and got some exposure to the field. However, I am quite new to machine learning and I have only an intuitive understanding of the ...
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k-fold cross validation and support vector machine

I have a dataset of 1877 rows and 6 independent variables and 1 dependent variable so the dimension of the dataset is 1877 x 7. Using the tune(svm,...) function I searched for the best gamma and cost ...
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Speech recognition (SVM) different signal lengths

I am developing a small project on speech recognition, the idea is to classify sound sources by Support Vector Machines. My dataset consists on 45 signals, however, they all have different lengths, ...
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Logistic PCA and the train/test split

I did a lot of readings about how to do PCA with train/test split. see PCA and the train/test split I understand that we should apply the PCA on train set and then apply the same transformation to the ...
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Increase in SVM Classifier performance after binning

I've been working on a classification problem, and I ran into something rather strange. The original problem has continuous features and three labels. I then mapped the continuous features to binary ...
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Can I make SVM ignore a feature by making all instances have the same value?

For Binary classification using SVM, I was wondering how to make SVM ignore a feature (e.g. the coefficient of that feature in the decision function become 0)? Will SVM automatically ignore ...
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Why can't scikit-learn SVM solve two concentric circles?

Consider the following dataset (code for generating it is at the bottom of the post): Running the following code: ...
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What is meant by “number of support vectors” in the SVM implementation of scikit-learn

I noticed that decreasing the C regularization parameter tends to increase n_support_ in the solution provided by ...
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Does distance from the decision boundary suggest higher confidence that the class prediction is correct using SVM?

Does further distance from the decision boundary threshold suggest higher confidence that the class prediction is correct when using SVM with probability estimates enabled? This is not a question ...
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When performing a svm hyper-parameter search for epsilon coef0 and degree which min max and increment values are suggested?

I am using libsvm, but I think this applies to any ML algorithm where these kernels are used. The default implementation of libsvm suggests values for the linear kernel or RBF kernel hyper-parameters ...
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Are there quantitative methods for determining sample size for SVM?

Are there any quantitative and justifiable methods to help choose minimum sample size for having a 'good' model from SVM? Logistic regression affords power analysis which provides minimal $n$ to ...
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Regularisation with SVM

I am working on a dataset in which I have thousands of binary features and a binary response. From the interpretation side of things I would like to fit a SVM model combined with some sort of ...
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Binary classification: does it make a difference to use zero or minus one as class label?

I'd like to build a binary classification model and I recall reading somewhere that the choice of the labels could have an impact depending on the algorithm. So the two modeling approaches are to ...
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How to apply svm to a dataset with a numerical (not categorical) dependent variable?

Context: I have some band values from a sentinel - 2 derived .tiff I now want to make a prediction regarding areas where i have no actual field data i.e. make a carbon map of sorts Libraries are: <...
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How does SVM decide the weight/coefficient of features?

I was wondering if someone can kindly provide me with some insights regarding how SVM (binary classification) decides the weights for the features. Say, I have a feature $f_1$ that appears in both ...
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huge difference between RMSE and MAE in non-linear regressors

I am building non-linear regression models such as a Random Forest regressor , a KNN regressor or a SVM using a RBF kernel and I decided to use both RMSE and MAE as evaluation metrics. I know that a ...
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SVR optimal hyperparameters are Epsilon = 0, Cost = inf?

I'm running an rbf-kernel SVR with GridSearchCV. I'm optimizing epsilon, cost and gamma. In my hyperparameter gridsearch, the optimal parameters appear "unbounded". Specifically, any epsilon under 1 ...
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What are some ways to improve the accuracy in this SVM?

I am an absolute beginner in the field of Machine Learning. I am trying to teach a binary classifier using the following dataset https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients ...
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Why is it called $\chi^2$ distance / kernel?

The $\chi^2$ distance function is defined as $$ \chi(u,v) = \sum_{i=1}^n \frac{(u_i-v_i)^2}{u_i+v_i} $$ and the $\chi^2$ kernel function, used in support vector machines, is $$ K(u,v) = \exp(-c \...
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PCA Before Random Forest [duplicate]

I am starting to work in data science and machine learning and I have a little question. I am trying to construct a model that predict a continuous variable, it means, a regression model. I have nine ...
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Is there any possibility of overfitting even after higher AUC, specificity and sensitivity obtained through repeated k-fold cross-validation?

I have built a model where a 10-fold cross-validation was performed 10 times. The average AUC, MCC, specificity, sensitivity of 10 times were reported as the prediction performance. Yet some people ...
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PCA shows overlapping boundaries, then why SVM performs best

I am trying to understand which model might work for a given problem before trying the models, I find this case against my knowledge. Please guide what I am missing. I am new to Data Science. Here is ...

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