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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Feature addition/ subtraction and SVM model accuracy

I am working on a text classification problem where I want to improve the accuracy of my model. I am presently using SVM with linear SVC and OneVsRestClassifier. The model should correctly predict all ...
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GridSearch on imbalanced multi-class dataset

I have an imbalanced multi-class dataset (GTSRB) and would like to use GridSearch to determine the hyperparameters for an SVM. As metric for the evaluation I chose F1 with average macro. ...
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how use RBF for primal model of svm?

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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How can I imporve accuracy for text classification and mapping using SVM?

I am working on a problem where I need to predict the text corresponding to another text in my training data file. For example: if I have value like the software in one of my columns and another ...
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Designing Custom Kernel from my Mathematical model

I derived a mathematical model for a porous system and the final function looks like this , after going through the Mercers Theorem and it condition for a kernel i would love to write a SVM kernel ...
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solving svm without using largeagian?

I wrote a SVM model in ampl. (multi classification). I am sure the model is right based on SVM. I didn't use lagragian just solved linear svm . But the result are not make sense to me . most of ...
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why I get very small coefficients in svm?

why I get very small number as a coefficient of svm ? how classification is done ? where I was wrong? I get some numbers like this: ...
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can we have different features for different hyperplanes in SVM?

is it possible if we have different features for different classes of svm? For example one of the hyperplane: $$w_1\cdot \text{age}+ w_2 \cdot \text{ trip duration} +w_3 \cdot \text{ income}$$ and ...
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SVM - Shuffle image data before GridSearchCV or not?

I have different image datasets, most of them are sorted by class, others are already mixed. For each of these data sets, I would like to train one SVM (in Python with Scikit-Learn), whereby in each ...
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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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31 views

Confusion on result of K-Fold Cross Validation and Independent Test set

I am relatively new in Machine Learning. I am using Random Forest and SVM for a project. Where I did a ...
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23 views

SVC classification not working at all on MNIST dataset

I'm sure I probably did something stupid but I'm trying to fit a simple SVC classifier on MNIST dataset as an example, and it completely failed by only predicting result 1 (sometimes 7 depends on how ...
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Why are the regions/decision boundaries overlapping with multi-class classification using SVM in sci-kit?

I am using the SVM in scikit-learn library for doing multiclass classification. I am wondering why these regions (decision boundaries) are overlapping (as seen in the picture below)? Could someone ...
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R SVM Predict - Error in predict.svm: test data does not match model

I started with a data frame of 23,515 rows and 3 columns. I split the data 70/30 into training/testing. I am fitting a classification model with SVM from the e1071 package to predict variable MISSING. ...
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Does it matter whether we put regularization parameter ($C$) with error or weight term in Kernel ridge regression?

Kernel ridge regression associate a regularization parameter $C$ with weight term ($\beta$): $\text{Minimize}: {KRR}=C\frac{1}{2} \left \|\beta\right\|^{2} + \frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|...
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Don't understand classification equation for hard margin SVM

I am trying to get a grasp of hard margin SVMs. In the lecture I am watching the professor talks about a classification equation which when a positive sample is input, returns a value of $1$ or more; ...
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How to Interpret output Coefficients with python sklearn Support Vector Regression?

I'm looking to interpret the output coefficients from my SVR model. For my case, the rbf kernel has the highest in-sample and out-of-sample performance. However, ...
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30 views

How to detect anomalies (errors and exceptions) in log files?

Is this a good approach? So I'm working on a Root Cause Analysis system which should help find the cause/the root error of failed system builds (packaged in a tarball), through the analysis of log ...
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190 views

Mathematical formulation of Support Vector Machines?

I'm trying to learn maths behind SVM (hard margin) but due to different forms of mathematical formulations I'm bit confused. Assume we have two sets of points $\text{(i.e. positives, negatives)}$ one ...
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33 views

Naive Bayes and Support Vector Machine (NBSVM) Classification

I am relatively new to datascience and have a question about NBSVM. I have a two class problem and text data (headlines from the newspaper). I want to use NBSVM to predict whether a headline has the ...
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29 views

Logistic Regression performing better than SVM with a Gaussian kernel performing better than a linear SVM

I am very new to machine learning. I am working with a data set, and my algorithm for logistic regression (with lasso regularization) is performing fairly well (~0.8 AUC), my SVM with a Gaussian ...
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27 views

Version of Perceptron

If we change the $ywx<0$ condition (for performing update) to $ywx<1$ like in SVM (but without adding regularization to maximize the margin), is there any difference from the basic perceptron (...
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Get sequential model to output probabilities with pystruct

I have implemented a sequential model using Pystruct. The model I use is BinaryCLF and as a learner the StructuredPerceptron. So far, when I test the prediction of my model, I give as an input the ...
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Confusion regarding prediction results of SVM and ANN on feature vectors

I am making a custom image classifier using Transfer Learning on Inception V3. I have 3 classes of images with ~6K images each. The input dimension of the network is 500X500 and the output of the ...
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44 views

Which performs better in time series forecasting, LSTM or SVR?

I have run LSTM and SVR models on various datasets having sample values in the range of 1-4000 and the MAPE obtained in SVR was consistently lesser than that obtained through LSTM. I was told the ...
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Regularization loss - Why is that important to get unique weights solution

There's something that is bothering me in Regularization. There is one bug with the loss function when I just use for e.g. a multi-class SVM loss. Suppose that we have a dataset and a set of ...
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What is the best reference for multi-class SVM?

Can someone suggest some papers about the multi-classification methods by SVM? One against all? A good survey or paper which ...
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19 views

Can we use kernel trick in primal problems?

I am wondering if we can use kernel tricks in primal problems in SVM? Without using dot product in dual problem? Also, I am wondering if we have additional constraints. Is there any paper or book ...
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1answer
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How are Lagrangian multipliers zero except for support vectors in dual representation of SVM?

How can we conclude that the Lagrangian multipliers are zero, except support vectors, in a dual problem? I cannot seem to see it. $$L(\alpha)=-\frac{1}{2}\sum_i \sum_j \alpha_i \alpha_j y_i y_j x_i' ...
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46 views

SVM behavior when regularization parameter equals 0

I read on this Wikipedia page the following about soft-margin SVM: "The parameter $λ$ determines the trade-off between increasing the margin size and ensuring that the $x_i$ lie on the correct ...
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Why kernel perceptron relies more on the training data than kernel SVM?

In UCSD machine learning course, it is said that: "for Kernel Perceptron, the solution is likely to depend on more of the training points than the ...
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Implementing SVM with Gaussian Kernel

This is referencing Prof. Andrew Ng's course on machine learning. In the part that details implementing an SVM with the Gaussian kernel, we are supposed to use all the training examples as our ...
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Learning curves for active learning regression

I'm applying a query-by-committee active learning algorithm to a regression task using SVMs and I would like to produce the learning curves for comparison with a baseline/passive/random learner. The ...
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Confusion regarding the Working mechanism of Activation function

For binary classification irrespective of the model used, the sigmoid function is a good choice for output layer because the actual output value ‘Y’ is either 0 or 1 so it makes sense for predicted ...
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Is there a non-linear implementation of SP-SVM?

I was trying to implement a parallel non-linear SVM in Python and I found this paper that has a review of different SVM implementations run across CPUs and GPUs and they mention they are focused on ...
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Should detection times get longer for dlib.train_simple_object_detector when I add more images?

I've played around some with dlib.train_simple_object_detector and have found that as I add more images the detection time grows longer when I later perform detections. Why is this happening? To my ...
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1answer
32 views

Get how similarity between the training data and the income data?

I'am trying to use Clustering and Classification methods as SVM using scikitlearn. I'm also studying some outliers/novelty detections I want something like a semi-supervised model. I want to predict ...
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1answer
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SVM hyperplane equations for linearly seperable data

I'm going through Wikipedia article on SVM and came across these equations: It says: With a normalized or standardized dataset, the parallel hyperplanes to our required hyperplane $wx+b=0$, can ...
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feature extraction for Radio signals classification

I found some code where the developer is trying to solve the problem of "Radio signals modulation classification" in this link link one of the solutions is using SVM to solve the classification ...
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Formulation of Optimization Problem in SVM

I need help in verifying/understanding a step in formulating an optimization problem used for support vector machines (though this question doesn't need any background in SVM). Consider a bunch of $m$ ...
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Interpretation of ROC AUC score

i tried to evaluate 6 models and after plotting , this what i get : So i'm wondering , if those results are "Right" ? Thank's in advance.
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Is there any conceptual relationship between 'kernel' in SVM and 'kernel' in convolution neural net?

In SVM, we have kernel function that maps an input raw data space into a higher dimensional feature space In CNN, we also have a 'kernel' mask that travels the input raw data space (image as a matrix)...
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Does one hot encoding in svm for nominal training set gets done by the weka package and package or library particular for svm by itself?

While using svm in java (weka) with nominal input set, does the inbuilt algorithm itself does the one-hot encoding or is it required by the user to do so?
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What do each of the three SVM classes in R represent?

Inspired by this post, I took a look at this doc SVN in R output this: ...
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193 views

What are the differences between SVC, NuSVC, and LinearSVC?

What are the differences between SVC, NuSVC, and LinearSVC? Please shed some light.
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1answer
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Hinge Loss understanding and proof

I hope this doesn't come off as a silly question, but I am looking at SVMs and in principle I understand how they work. The idea is to maximize the margin between different classes of point (within ...
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8 views

Normalization in SVM classifier

I am trying to normalize my features for a classification model with 3 class outputs. There are two kinds of features. First is medical test results and second is patient information such as age. The ...
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SVM classifer gives the same output for all test cases

I have a dataset belonging to seven different classes. Each input consists of 1024 features. There are only 30 samples belonging to each class available for training. I have used several ...
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How is hinge loss related to primal form / dual form of SVM

I'm learning SVM and many classic tutorials talk about the formulation of SVM problem as a convex optimization problem: i.e. We have the objective function with slack variables and subject to ...
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Theoretical supremacy of Gaussian SVM

Many packages such as sklearn use RBF kernel as default for SVM classification. I wonder if there is a proof/explanation why this is the "best" (I am aware of the no free launch theorem) kernel for a ...