Questions tagged [svm]

Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.

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Generalized quadratic loss learning

I'm studying a binary classification task with an objective function, derived from SVM, defined so: $\vec{\xi}' S \vec{\xi}$ with: $y_i (f(\vec{x}_i)) >= 1 - \xi_i, i=1..l$ and: $\xi_i >=0,...
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How to select best features SVM- numerical inputs and categorical output

I have a number of features and I want to reduce the dimensionality to ensure good model accuracy. How do I select the best features where all the inputs are numerical and the outputs are categorical. ...
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How to select the best features for Support Vector Classification

I have a feature set that contains approximately 2 dozen features of technical analysis indicators. My own domain knowledge tells me that some of these features are better than others for predicitive ...
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T-SNE good clustering but SVM classification poor

I am trying to classify in 4 different classes, paragraph embedding vector computed with doc2vec using an non-linear svm over them. When I visualize the embeddings using tensorboard t-sne I can see ...
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Machine Learning classification problem

I'm trying to do this classification problem, depicted in the following Figure. The task is to separate the blue elements from the red elements in a cartesian (x,y) coordinate system. I have to: ● ...
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Logistic Regression vs SVM

Following Andrew Ng's machine learning course, he explains how we can modify logistic regression to obtain SVM algorithm. First he replaces (sort of approximating) cross entropy loss with hinge loss ...
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In an SVM, does a more negative/positive decision score mean that it is further from the seperating hyperplane?

For example, if I have a sample with a decision score of -6 and another with a score of -3, which sample is closer to the hyperplane? Also, does the probability of a sample belonging to a class ...
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Support Vector Machine (SVM) for classification problem based on Earth Mover's Distance (EMD)

I would like to run SVM for my classification problem using the Earth Mover's Distance (EMD) as a distance measurement. As I understood the documentation for Python scikit-learn (https://scikit-learn....
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Plotting ROC & AUC for SVM algorithm

Towards , the end of my program, I have the following code. model = svm.OneClassSVM(nu=nu, kernel='rbf', gamma=0.00001) model.fit(train_data) Output ...
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Many separation line using RBF kernel in SVM

Below is my code, it take a range of a number, creates a new column label that contains either -1 or 1. In case the number is ...
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1answer
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Reformulating the maximal margin classifier optimization problem

Ok, so I've been trying to read up on how SVM's work and started with maximal margin classifiers. At page $132$ in ESL (Elements of Statistical Learning) the authors "reformulates" the optimization ...
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What is done first, cross validation or grid search?

When I have the data set to train a model with SVM, which procedure is performed first, cross validation or grid search? I have read this in a couple of books but I don't know in what order all this ...
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LibLinear Parameter optimization

I want to use LibLinear to learn a model from 500,000 training samples (equally distributed over 8 classes) and test on 1,300,000 samples. The data set contains around 60 standardized features. In ...
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SVM SVC: Metric for parameter optimization on imbalanced data

I trained a multiclass SVC with RBF kernel on a down-sampled (and therefore balanced) dataset. Now I want to perform grid search to find best cost and gamma. What performance metric should I optimize ...
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Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
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If a categorical feature only occurs a few times in a data set, should I drop it?

I have a data set of mostly categorical variables. When I one-hot encoded them some of the features occur less than 3% of the time. For instance the Tech-support feature only occurs 928 times in a ...
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1answer
52 views

Difference between SVM and GD/SGD?

My colleague mentioned that a data science project is using SGD classifier. So I started reading about GD/SGD and came across a nice article about Text classification using SVM and GD. In the end of ...
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Keras : How to Connect CNN ResNet50 with svm/random forest classifier?

I want to classify multiclass (10 classes) images with random forest and SVM classifier, that is, make a hybrid model with ResNet+SVM , ResNet+random forest. My ResNet code is below: ...
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1answer
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Linear Discriminant Analysis (LDA) before or after k-fold cross-validation?

I have features extracted from a small dataset, would like to reduce the dimensions by using LDA. Also want to do a SVM classification with k-fold cross-validation. My question is: What would be the ...
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difference between empirical risk minimization and structural risk minimization?

I understand the meaning of empirical risk minimization as separate topic and was reading about structural risk minimization, it is hard for me to understand the difference between these two. I read ...
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SVM C vs gamma hyper tuning

While running SVC(), how we can hyper tune C vs gamma combination? I could see changes in C and gamma are impacting the accuracy differently. Also what i understand about C and gamma are : 1) C is ...
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Kernel selections in SVM

I want to understand the kernel selection rationale in SVM. Some basic things that i understand is if data is linear then we must go for linear kernel and if it is non-linear then others. But ...
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Equivalent procedure to Scikit-Learns class_weight=balanced in Keras?

I want to train a SVM and a CNN with the same unbalanced multiclass-dataset and want to compare the results. I use Scikit-Learn for the SVM and Keras for the CNN. My goal is that no class is ...
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How to implement Hinge loss in Support Vector Machine with SGD

I implemented a Support Vector machine as follows : Where J(Theta) is the Objective function. My code : ...
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How to create a positive definite matrix from Dataset for solving svm dual optimization problem?

I try to implement a SVM from the scratch by myself and facing some issues when solving the dual optimization problem using qpsolvers. So I created linear separable data with sklearn ...
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NLP and one-class classifier building

I have a big dataset containing almost 0.5 billions of tweets. I'm doing some research about how firms are engaged in activism and so far, I have labelled tweets which can be clustered in an activism ...
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How to Manually Classify using SVM?

Consider points x1 = (1,1), x2=(1,0), x3=(1,-1) from class C1 and points x4 = (-1,1), x5=(-1-1) from class C2. Classify the given data with SVM How do we manually classify data by finding the ...
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How to create an roc plot and calculate AUC for an svm (that does not return probabilities)?

I have some SVM classifier outputting final classifications for every sample in the test set, something like 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1 and so on. The "...
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4answers
273 views

ROC curve interpretation

I trained a CNN model and a combined CNN-SVM model for classification. I wanted to compare their performance using ROC curve but I was confused which model is better. How to interpret the given ROC ...
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1answer
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How can different classification algorithms expressed as neural networks?

I have heard that each of the different classification algorithms can be expressed as a neural network architecture. How can the different algorithms like Logistic Regression, SVM(Support Vector ...
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What is the difference between LS-SVM and P-SVM?

What is the difference between least-squares SVM (LS-SVM) and proximal SVM (P-SVM)? How does the decision boundary change in case of both of these types of SVM?
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Genetic algorithms: what connection to support vector machine / naive bayes

I found the following list of seven classifiers: Linear Classifiers: Logistic Regression, Naive Bayes Classifier Nearest Neighbor Support Vector Machines Decision Trees Boosted Trees Random ...
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SVM, which range to use when normalizing

I am using the SVM classifier from Scikit Learn. I was wondering is there is a know-best-practice when it comes to normalization. I'm using different normalization tecniques, but all my normalized ...
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Machine learning algorithm for classifying a 2xN array of ranged coordinates?

Good afternoon, I have a dataset of lists of coordinates that are ranged from (0, 100) on the Y-axis and (0, 300) on the x-axis, with double precision. I'm looking into classifier algorithms that ...
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In SVM, is the support set still small if kernel trick is used?

In SVM, we classify y based on whether f(x) > 0 or f(x) < 0. I understand that in SVM with f(x) being linear in x, the support set is typically small (i.e., the number of support vectors is much ...
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Which scoring for GridSearchCV is best, when imbalanced multiclass dataset?

I have an unbalanced multiclass dataset (GTSRB) and want to optimize the hyperparameters of an SVM through GridSearchCV. I know that accuracy is not suitable for scoring in this case. Which evaluation ...
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How can I classify single fused gray scale image in python ? is it possible to binary classsify single output image with Ground Truth image?

I want to calculate the precision, recall, and accuracy of the single predicted image(y_pred) with the Ground truth (y_true) image. I have only two binary class (0 and 1) so my question is, ** is it ...
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Do not scale Hog features?

when I train LinearSVC with the Hog features extracted from the Fashion-MNIST dataset then I get better results if I don't use StandardScaler before training than I use it. ...
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1answer
181 views

K-fold-cross-validation if training dataset is much smaller than test dataset?

I'm a beginner in machine learning and I have a special case in which I have only a small training dataset of about 500 images and a test dataset of 10,000 images. Does it still make sense to do a 10-...
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Feature importance in SVM

Why is there no command for feature importance in SVM like the one provided in Random Forest feature_importance_ from ...
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How can I map the sample from the original feature space to the new kernel feature space? (Sk-learn)

Let's say I have a very basic SVM model, implementin sk-learn: clf = SVC(kernel='rbf', class_weight=weights, gamma=gamma) clf.fit(X,y) X is the sample space with ...
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1answer
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Non-Convex Constraints for Classification Problems

I am willing to create a hypothetical non-convex constraints for the purpose of practising nonlinear classification using an algorithm. I thought of such constraints in the form: $x^TAx + Bx \leq c$. ...
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How to upload a sklearn SVM model as a chrome extnesion?

I have trained an SVM/Logistic regression machine learning model using its scikit implementation. But now I want to do the same with Tensorflow/Keras. This is for easy conversion to Tensorflow.js. ...
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How to deploy machine learning models as a chrome extension?

I have trained a stance detection model using SVMs. Wanted to know how can I deploy this as a chrome extensions. I do understand that the question is a bit broad but any links, suggestions etc. will ...
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Implicit feature selection

I have heard that Random Forest and other tree based machines apply some kind of implicit feature selection. My Question is: Does this also apply for machines like the SVM? As far as I understand is ...
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(Scikit-learn) differences between LinearSVC, 'linear' kernel SVC and poly kernel SVC with degree 1

I would like to know the differences between: linearSVC() SVC(kernel='lineaer) ...
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34 views

SVM hyperplane margin

so that $H_0$ is equidistant from $H_1$ and $H_2$. However, here the variable $\delta$ is not necessary. So we can set $\delta=1$ to simplify the problem. $$w\cdot x+b=1 $$ and $$w\...

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