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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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how we can constraints in dual of lagrangian?

I am confused about the general rule of Lagrangian multiplier. (which usully use for SVM). I could not find a good book or paper that explain it completely and clearly. Suppose we have $$minimize: ...
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SVM on small dataset: no difference in loss value after feature selection and small lambda

I am running an SVM on a very small dataset of 56 items, 20 features and 4 classes. I want to know which features are the most important for classification, and how they interact (no future real ...
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SVM Optimization

Consider a Classification set up where there are $n$ covariates represented by $x_i \in \mathbb{R^{n+1}}$ and $x_{i1} =1$ . While $y_i \in {\{-1,1\}}$ defines the class where $x_i$ belongs to and ...
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How does a kernel function work in SVM?

I am really confused how kernel trick dosen't affect to prediction of linear equation! I know what SVM is and I know the concept of a kernel function in SVM. What I really cannot understand is the ...
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can we use SVM with multiple fuction for some classes?

I am wondering if we can combine the functions in classification with SVM. Suppose we have 4 classes A B C and D. for multi classification problem the SVM (one against all) works as the following ...
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What do we call models that output probabilistic scores vs. models that output non-probabilistic scores?

In machine learning -- specifically in binary classification -- there are models that output a probability for each data-point fed to the model. For example, a logistic regression could take some ...
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How can I use receiver operator curve in this SVM classification problem?

Short description of the learning task: I have a corpus containing voice segments annotated with the mean BPM obtained from heart rate recordings. For example, one sample would be like 5 s audio, ...
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Accuracy of SVM prediction

I'm trying to build a text classification model with SVM. The training data set consists of 100 string records with a one-to-one mapped response variable which is also a string. I can't split the data ...
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Variable vector length (SVM)

I am classifying vectors of varying length using LinearSVC in scikit-learn. However, it seems like it needs all the training vectors to be of the same length. What is a good approach to get around ...
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CS231n SVM Optimization : Mini Batch Gradient Descent

I was doing CS231n assignments and found a very interesting implementation of mini-batch gradient descent for SVM image classifier assignment. It is, ...
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How to use a scoring metric other than rsquared for an SVR? [closed]

I've searched through the previous questions and I can't quite find what I'm looking for. Perhaps I'm phrasing the question incorrectly, so if that's the case I do apologize in advance. I'm trying to ...
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Support Vector Machine Imputation (SVMI) in R?

I would like to use SVMI to impute missing data in a file. Is there a command in R that performs this function? Thanks.
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Forecasting time series data using EEMD based SVM?

Splitting of Dataset: Dataset = Train1 + Test1 EEMD(Train1) = train1 + test1 I am forecasting on time series data("Dataset") using SVM. First I found the Intrinic Mode Function(IMF) of time series ...
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svm loss function gradient

I was taking Stanford's cs231n class and was unable to understand the gradient calculated using the SVM loss function. You should go here to check the notes which I am talking about. This is the SVM ...
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Large scale SVM classification problem

Problem I am now working on a sentiment analysis task where the largest dataset involves 36 million custom reviews and associated sentiment (positive or negative). The feature extraction process is ...
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Crammer and Singer Dual SVM Problem: derivation

I am trying to prove the dual form of the Crammer and Singer SVM problem and I failed to reach the dual problem correctly. Both primal and dual problems are found in this paper in page 12 https://www....
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Scikit-Learn SVC Porbability Function

I use scikit-learn to train a SVC with 'poly'-Kernel and propability-paramter enabled. Most of the time the prediction and the probability assigned to the prediction is correct. That means: ...
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Does collinearity of two features affect the predictive performance of support vector classifier?

I have a set of features for my machine learning model (support vector classifier, SVC), two of which are strongly positively correlated (i.e., diameter and spherical volume). Does this affect the ...
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Selecting the best subject's data and features to optimize the analysis

I am not good at statistical analysis. So I am posting here my case and looking for your kind suggestions. My case: I have data from subjects, which each subject has two similar runs that were ...
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how to solve quadratic programming in SVM

In SVM we have $$\max_{\alpha}1^T{\alpha}-\frac{1}{2}{\alpha^T}Q{\alpha}$$ $$s.t.\quad{\alpha^T}y=0,\quad{\alpha} \ge0 $$ If we have 3 data points and 2 dimensional example in SVM the Q matrix ...
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How are the various guarantees provided to SVMs by Statistical Learning Theory affected by the Kernel Function

I never studied the field in depth, but I am very aware that state of the art performance in most ML tasks is now achieved by various flavors of neural networks. At the same time, Vladimir Vapnik, ...
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Estimating conditional expectation using SVR

I have an estimation problem: $X_t=f(S_t)=E(X_T\mid S_t)$ given the observations $(X^i_{T},S^i_{t})$ for all $i$'s. I used support vector regression (SVR) to run a regression of $X_T$ on $S_t$. Here $...
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How to calculate the Langrangian of the hard margin SVM primal problem?

I need to compute the Lagrangian of the primal problem for hard margin SVMs by hand. This is an assignment for university! I have vectors $$x_0 = (0, 0), x_1=(1, 2), x_2 = (-1, 2)$$ and $$y_1 = -1, ...
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Empirical Comparison: which ideal data characteristics are best captured by each type of machine learning model?

I have reached the point as a data scientist where the empirical differences between the different types of regression models (leaving out classification only for simplicity) have started to matter ...
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SVM in the classification layer of a Feedforward neural network

I want to use SVM in the classification layer of a 2 layer feedforward neural network. Need guidance from the community on how to approach this problem. This involves capturing the features from the ...
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how svm from e1071 works with k>2

I'm pretty new to R and machine learning and I'm doing an analysis on a dataset. I'm tryng to use the svm function from the e1071 library but I'm wondering if it takes care of number of classes. I ...
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What are other nonlinear transformation methods in machine learning except Neural Network activation functions?

One advantage of the MLP neural networks is the nonlinear transformation used on raw features. The popular ones used are the activation functions like Sigmoid, Tanh, ReLU, Leaky ReLU, etc. They are of ...
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Linear separation in higher dimension

I am having a problem comprehending with the relation of kernel, weight and linear separation. I have a case where I am given a kernel $k_1$. that has a corresponding mapping $\phi_1$. And we ...
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Finding best kernel parameters in support vector machines

I am following this tutorial: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/ , proposing an application of support vector machine to the well known Iris ...
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Can we get resonable classificaion if we use RBF kernel for linearly sepable data?

I have a dataset composed of some target and non target. I have used Support Vector Machine to classify. I am taking 50% data for training and testing all data. If I use linear kernel, I get very ...
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What is the basis for the default sigma value used by svmRadial in caret? [closed]

I am looking at the source code (I think) for the "svmRadial" function in the caret package. It looks like the default sigma values are calculated by first using the kernlab package's "sigest" ...
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Which algorithm should I use to predict the winner/loser of a competition, among 5 competitors?

I hope I posted in the correct session. I need to solve this "simple" problem. PROBLEM EXPLANATION I need to predict who is more likely to win a car race, among 5 drivers. I have a database that ...
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svm model caret package

I am trying to reproduce the svm model example reported in the original caret paper by khun. But it runs forever and I don't get the output. not even after 2 days. What's wrong? this is the paper: ...
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SMO alternative for support vector machine

I've used sequential minimal optimization (SMO) for solving the SVM dual formulation. I'm wondering if anyone has a good suggestion for an alternative algorithm for the same problem but with a ...
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combining confusion matrix

I'm doing multi class SVM classification on MNIST data. I'm using one vs all approach, so I'm basically doing the binary classification for 1-9 digits vs digit 0. I'm wondering how to combine their ...
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Svm soft classifier guaranteed to separate the classes

Hello I have a question about support vector machine in my data analysis course. I am given the following statement: a SVM soft margin classifier with the cost parameter C > 0 to a data set that is ...
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Why is Hard-margin SVM training a minimization problem rather that a maximization problem?

I am looking at the wikipedia article for hard-margin SVMs and it looks like the optimization problem they use is "minimize ||w|| such that the classes are linearly separable" However isn't the ...
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Reproducing kernels: how do I numerically compute the decomposition?

Suppose I'm given a kernel, $$ K(x,y) : \mathbb{R} \times \mathbb{R} \rightarrow \mathbb{R} $$ In order to describe/understand the (unique) associated RKHS, I seek its eigenfunctions, as per Mercer'...
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Soft margin SVM seperability

I have a set of data points of the following form: - - - + + + + if $C=0$ then how many support vectors do we have? if $C= \infty$ then how many support vectors do we have? if $C=0$ then its a ...
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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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Hinge loss 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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Using SVR to model simple data set

I have a fairly simple data set that I gathered from running workloads on a Hadoop Cluster. My goal is to model the running times of this application based on the feature variables of interest. The ...
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When kernels are not useful in SVM?

In SVM using kernels we map the original features to the higher, transformer space (feature mapping) and then perform linear SVM in this higher space. But when kernels are not useful? I could not find ...
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Question about the location of regularization constant C in SVM

I've encountered very similiar but different functions in SVM optimization problem, the diffrence is in the location of regularization constant C. $\sum_{i=1}^n(1-(y_i(w^tx))_+ +\frac{1}{2C} \left\...
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Can SVM with Gaussian RBF kernel separate all kinds of data theoretically?

Gaussian is well known because its corresponding feature mapping is to infinite dimension. So with finite number of training data, is that the case that we can achieve zero training error with some ...
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Need for explicit overfit to gain optimum Leave-One-out results?

For an SVM classification with only few (~150) datapoints but ~100 features, I have created a Leave-One-Out setup in order to mimic the classificator performance on sort of unseen datapoints. From ...
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Is it acceptable to test on 0.01% of the training data?

I'm doing a cross-corpus evaluation on text classification with a LinearSVM. I was wondering if it is acceptable to skew the training-testing split more than the usual 80-20% split. Specifically, I ...
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SVM one class outlier detection/classifier with Matlab

I have several independent time series (a small sample is in the end of the question) and I am trying to find the outliers using SVM. I have used this to find the outliers ...
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How to fix this error p<0! while optimizing SVM using PSO? [closed]

I am trying to optimize SVM paramters for a regression problem using PSO in R. I am getting this weird error. Any suggestion on how to fix this error would be greatly appreciated. Error in svm....
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Selecting SVM parameters if training data is oversampled/undersampled

I am working on classification for highly imbalanced data. Let's say I have a strategy to oversample/undersample the training data. I plan to use an SVM classifier to perform the classification. Now, ...