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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Binary classification Task : Least Squares kernel regression(squared loss) Vs SVM (hinge loss)

In binary classification, the solution function, in order to fit the training data, it just needs to acquire values that have the same polarity as the desired values, rather than accurately acquiring ...
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18 views

Uniqueness of svm solution

How does one show that if the solution of the primal linear SVM is unique, then there exists a support vector whose corresponding slack variable is equal to $0$? I tried showing this through proof by ...
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Number of Kernels in LSSVM

I'm studying LSSVM algorithm at the moment, and find one nuance very strange in most all papers I've read so far. The input/desired output are usually described as a set of {Xi, Yi}, where i = 1 .. N,...
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How do I create a classification model to predict observations with different feature vector lengths?

Say I have a dataset containing hourly records of the vital signs of people trying to survive in the wild, their environmental conditions, and a label of whether the person survives. I would like to ...
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60 views

Feature scaling dramatically improves performance

I am working with "Forest Coverage Type" Kaggle dataset (https://www.kaggle.com/c/forest-cover-type-prediction/data) and have applied support vector machine classification to predict forest coverage ...
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49 views

What are w and b parameters in SVM?

I've read almost every article on the web, every question regarding SVM here, but I still don't get how to calculate w and b, how did they appear in formula, what is weight and what is bias: $$\vec{w}...
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204 views

Why are random Fourier features efficient?

I am trying to understand Random Features for Large-Scale Kernel Machines. In particular, I don't follow the following logic: kernel methods can be viewed as optimizing the coefficients in a weighted ...
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17 views

Choice of the learning algorithm for Recursive feature elimination

I have a dataset that I divide into 80% for training+test and 20% for validation I have been using Recursive feature elimination for feature selection with SVM on the 80% partition...
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Rain overflow modeling: Categorical variables or separated models?

I'm working on a project where I have to predict rain overflow due to rain for 5 sewer locations. I have a file which tells me if there is a rain overflow (=1) at a given date for a given sewer or no (...
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23 views

How does QSVM algorithm differs from SVM?

Where does the working of QSVM differs from classical SVM ? And how it is fast ? During which steps of the algorithm do they differ ?
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Maximizing payoff when a 'dont care' state is present

Consider the following: We have a training dataset ($y_i$, $\mathbf{x}_i$) $i \in [1,n]$ where $y_i \in \{-1, 1\}$. We can build any model (logistic / SVM / anything else) to predict $y_i$ given $\...
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ROC curve under diagonal?

I trained an SVM to classify images based on some extracted features (using the ISIC dataset). The resulting ROC curve produced by sklearn looks like this: I have don't quite understand the line for ...
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Consistency on SVM results

I have a conversation in my office regarding the results on SVM classification. As far as I understand it SVM does not contain any random initialization that could produce a different result on ...
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Why am I getting accuracy of 100 percent using SVM

I am working on Credit card data set for fraud detection. When I apply SVM for it, I am getting the accuracy as 100 %. What might be going wrong here? Here is the code ...
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Preprocessing during a Kfold cross validation

I have noticed from various sites online that preprocessing and feature selection when dealing with Kfold Cross Validation suggest that the preprocessing and feature selection should be done on the ...
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Tuning SVM when extracting features from DCT?

What will be the parameters for "SVM" classifier when extracting features from Discrete Cosine Transform (DCT) and classifiying features using SVM considering having 200+ classes?. What should be a "...
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239 views

What is the support vector machine?

What IS the support vector machine? Can someone clarify my confusion? Possible answers: The SVM is the problem: given data $(x_n, y_n), n = 1, \ldots, N$ $$\min_{w, b}\frac{1}{2}||w||^2$$ $$\text{ ...
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What statistical classifiers can use unlabelled data to enhance their performance similar to a transductive support vector machine?

I was wondering if statistical machine learning methods like tree based methods, ANNs, logistic regression can make use of unlabelled data to enhance their performance similar to the way a ...
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What is the role of C hyperparamter in SVM? [duplicate]

Why increasing C hyperparamter the margin gets smaller?
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Why does the formulation of the SVM problem has the bias (something we try to optimize) as a part of the constraint?

The common formulation of the SVM problem is $$\min_{\theta, \theta_0}\frac{1}{2}||\theta||^2$$ $$\text{ subject to: } y^{(t)}(\theta \cdot x^{(t)} + \theta_0) \geq 1, \ t=1,...,n,$$ However, it ...
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24 views

Is SVM RBF applied to both classes?

Lets say i have following 1D data (position on x), color is target class and I need a classifier which classifies green from red: I decided to use SVM. Data is clearly not linearly separable, so i ...
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55 views

Gaussian RBF vs KNN explanation

I was studying SVM ML alghorythm and I was wondering about solution for non-linear cases. As I understand it for know, SVM tries to find hyperplane or object in defined n-dimensional space, which ...
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Plot Actual vs Predicted SVM Regression

I am building an SVM regression model using caret package, however, I am not sure what is the best approach to plot predicted vs actual values. I have the code below. You can reproduce the output by ...
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Compute Bias Term in Support Vector Regression (SVR)

I'm trying to figure out how the bias term in SVR is computed. I've already optimized the alphas, so I have a Nx1 vector for a_i and a_i*. Furthermore, I have the predictor matrix X which is Nx2 and a ...
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Does reducing dimensionality of data makes it less linearly separable?

I recently read about kernel trick in SVM that says that mapping data to higher dimensions makes it more linearly separable but can we conversely say that "mapping ...
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does the resolution of the vectors influence the result?

I have setup a model for prediction of free parking places. Thi model based on SVR (support vector regression) and sklearn libs. My feature vectors are: time as float (e.g. 8.00, 8.25, 8.5, 8.75, 9.0....
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Machine learning model giving different probability on different scenarios [closed]

I am working on Iris dataset classification model. I am using SVM model and getting the probability of each class using predict_proba function. I tried the same example on different mediums ...
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Separating Hyperpanes - The Elements of Statistical Learning

The authors say that the green line depicts a hyperplane or affine set $L$ defined by the equation $f(x) = \beta_0+\beta^Tx = 0$; since we are in $R^2$ this is a line. One property is: For any two ...
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Question about one example in Burges' SVM tutorial

On page 127 of Burges' SVM tutorial paper [1]. He listed an example to validate the bound for true error rate. However, it seems to me that the left side of equation (8) $\frac{m}{4l}\leq \ln(2l/m)+...
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47 views

Is the kernel trick unnecessary for for non-linear SVM?

I am just learning about Mercer Kernels, and a question came up. Since using Mercer's theorem, we know that a positive definite kernel matrix can be represented by an inner production of the input ...
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feature importance for SVM with a nonlinear kernel

Using sklearn, I did SVR using rbf kernel. Though I got good results, problem is I don't know how to get the important feature that the algorithm used. Also coef_ ...
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In semi-supervies learning, is “low density separation” the same thing as “pseudo-labelling”?

I'm looking into the different methods of semi-supervised learning. In the wikipedia page, one of the methods described is called "low-density separation", where we attempt to minimize this loss ...
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33 views

Object classification

I'm currently working on a "Where's Waldo" project as part of my coursework, where I have to find 3 different characters in any given image - Waldo, Wenda, and Wizard. I'm trying to convert this ...
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41 views

SVM formulation

Here is an extract of an article. They are solving a One Vs One classification problem. What does "y" stands for ? It is a bias ? The way they are expressing the minimization problem is quite ...
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How to classify new data point for Kernel SVM?

I am working on implementing the kernel SVM using cvxopt quadratic programming, this is for a class so that why I'm not using something like SKLearn. Assume here no slack variable, only SMV but with ...
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21 views

Support vector machine cost function mathematical representation

I am trying to understand the mathematical representation of the cost function of a multi-class SVM. The representation I am looking at is: $J(\theta) = \sum_{i=1}^{m}\sum_{j\neq y^{(i)}}^{m}max(0,\...
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What's the structural difference between “Stacked” and “Deep” systems?

How are the stacked learning algorithms different from deep ones e.g. Stacked SVM and Deep SVM?
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One-Class SVM for anomaly detection on time series

I want to use One-Class SVM to detect anomalies on univariate or multivariate time series. The problem is that SVM can be only applied to a set of vectors and are not aimed for time series. The paper ...
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1answer
53 views

SVM probability output threshold as (1 - FPR)?

I have a binary SVM with probability output (via Platt scaling). I want to set a threshold on the probability outputs since I want to trade off making false positives/negatives. Is it possible to ...
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18 views

SVM and RBF Kernel

I have read that high gamma value in SVM(rbf kernel) can lead to high bias. But I have seen high gamma overfits the decision boundary. Why is it not called high variance?
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Influencing linear SVM features with artificial feature vectors to prevent overfitting (pseudo manual feature selection)

I'm training a linear SVM with about 20 features with the usual setup (10 fold cd, grid search, data standardized to mean=0, sd=1, positive class label=1, negative class=-1, balanced examples for both ...
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69 views

what steps to take to get better performance

I have a data frame like this : ...
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Support vector in SVM

As I have studied that support vector in SVM, is either on margin on inside margin. But during an exercise on simulated set, I am getting a support vector very far away from margin. Here is the ...
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How to model dependency between input features when building a classifier

I have dataset with the shape of (1000,20) (1000 rows, 20 features) and I want to build a classifier for it. However, most sk-learn algorithms assume the these 20 features are independent. In my ...
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1answer
25 views

How to compare the performance of different SVMs and CNNs?

I'm a beginner in machine learning und I have a problem to find the best way to compare the performance (accuracy) of different SVMs and CNNs (Jupyter Notebook)? The CNNs I train in Google Colab with ...
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1answer
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How does square of dot product can be compared to a similarity in terms of circle

When reading about SVM and Kernel Trick, a common similarity function which is often used is which is taken from this blog I am trying to understand how this is related to equation of circle. ...
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One-Class SVM - Decision function

The following is based on the paper: Schölkopf et.al - SVM for Novelty Detection First let us consider the (classical) Soft Margin SVM optimization problem: ${\displaystyle {\text{minimize }}{\frac {...
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538 views

Error while performing multiclass classification using Gridsearch CV

I am trying to solve a multiclass classification problem using SVC as the base estimator and GridSearchCV to tune my results. Mentioned below is the code and the error being received: ...
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(SVMs) Do the specific higher dimensional mappings of attributes not matter when calculating a kernel?

From what I know, one of the strategies employed by an SVM is to increase dimensionality of your data until they are linearly separable. (I guess there's some mathematical proof that your data will ...
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Figuring out the margin for the soft margin SVM (exam question)

This is an exam question and I am not sure whether it is solveable with the given information. We were given a graphic that displayed binary labelled points $x^{(i)}\in \mathbb{R}^2$ with $y^{(i)} \...

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