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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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SVM & KKT relation

I want show that the dual of the maximum margin problem can satisfy the Karush–Kuhn–Tucker conditions. When the inequality constraint is inactive ($a_i > 0$) and using the lagrange multiplier ...
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Gaussian Kernel for SVM [on hold]

I have recently learnt about use of kernels in SVM. I encountered a question but I am unable to visualize how it can work. The question is to give an example of data points (like in a 2D plot) for a ...
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SVM: What is relationship between the number of features and the number of dimensions?

I have implemented a support vector machine (SVM) in Python. I want to know the relationship between the number of features and the number of dimensions. My dataset contains 5 features, does it mean ...
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How to do out of sample forecast by SVM in r? [on hold]

I am doing uni variate forecasting by using SVM in r. I did my in sample forecast precisely but when i do forecast for some next time period it gives the same values. here are codes. ...
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What is the intuition behind changing the dot product for another inner product in SVM?

I understand that, when classifying with a SVM using a non-linear kernel, we are basically changing the dot product for a "custom" inner product. Is there some reason for working with a different ...
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Fitting probability boundaries of a binary output

I work on a problem for which the output is binary. The class are not linearly separable and quite unbalanced (few ones are embedded in a lot of zeroes). I want to classify the data by levels of ...
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1answer
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Can we predict the monthly sales amount of the coming month without knowing the values of the independent variables of the coming month

I have a data set where the monthly sales of TMT bars and various other explanatory variables are present from April 2014-March 2018. I need to predict the monthly sales of the coming/next month. ...
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Can someone provide a detailed explanation of some aspects of the kernel trick?

Almost all answers I see mirror this one: "Suppose we have a mapping φ:Rn→Rm that brings our vectors in Rn to some feature space Rm. Then the dot product of x1 and x2 in this space is φ(x1)Tφ(x2). A ...
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Improvements on using factorization machines?

I am fairly new to factorization machines, I have read papers about it and seen examples of it online. My current goal is to solve a recommendation problem and I'm not sure if what I'm doing is ...
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k-means clustered data: how to label newly incoming data

I have a data set with labels that were produced by a k-means clustering algorithm. Now there is some data (with the same data structure) from another source and I wonder what is the most sensible way ...
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15 views

SVM non-linear decision function using hyperline

Suppose that we have a toy classification problem X -> y in 2D. In scikit learn, I solve this question with X = np.array([[2, 1], [3,1], [3, 0], [4, 0], [5, -1]]) y = np.array([0, 1, 1, 1, 0]) from ...
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Why is it that Support Vector Regression isn't as popular as SVM?

I know that SVM and SVR have different purposes in that the former is used for classification while the latter is used for regression, despite the similarity in the concepts between the two. However, ...
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image dataset for Comparison of SVM and CNN

I'm looking for a suitable image dataset to train an SVM, a CNN and possibly an MLP as classifiers and to compare the results. Since an SVM archieves good results with small data sets and a CNN and ...
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Number of weights/parameters needed to store a trained Gaussian Support Vector Machines model for binary classification?

I have been trying to make sure I understand this answer right The prompt states: "We trained a SVM classifier which takes input vectors (with N features) and does binary classification using a ...
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Estimate RBF-kernel mapping function given graph/space

Problem Provide a mapping function $𝜑(x)$ that enables us to draw a linear separator between the two classes in the mapped space. Attempt I tried to use a radial basis function by finding 4 ...
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22 views

How can we interprete the results generated by SVM?

I am using SVM for classification purpose. I got results but I am not understanding how to interpret its results and also how can I know the contribution of each independent variable in the prediction ...
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Svm grid search tunes itself to 100% training accuracy?

Im using rbf svm classifier with nested cross validation (5 kfold to tune hyperparameters and then leave the last 10% for testing). When tuning hyperparameters the best cv accuracy trains to around 56%...
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2answers
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How should I resample the training and testing set with imbalanced data whilst having meaningful performance metrics?

I have an imbalanced dataset of approx. 200 positive and 800 negative examples. I run nested cross-validation where i=5 and j=5; (i is inner and j is outer). The cross-validation procedure isn't the ...
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1answer
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sklearn Support Vector Regression - test data prediction is constant

I am just getting into learning some basic machine learning for a project at university and I am having a little trouble with SVR on sklearn. When training a model I can change the epsilon value and ...
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21 views

Soft Margin constraints in SVM

I have understood the constraints of Hard Margin SVM but stuck at Soft Margin SVM. The Objective Function along with constraints of Soft Margin SVM is given below. such that and The slack ...
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Coefficients in Optimal Separating Hyperplane(SVM)

This question is closely related to Elements of Statistical Learning p.132 - p.134. I want to reproduce the , in p.129 and p.134, respectively. This is a toy example without given any data, so I ...
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1answer
21 views

norm of SVM's weights vector

From solving a hard-margin SVM primal problem we get: $$ w = \sum{\alpha_i y_i x_i} \\ \sum{a_i y_i} = 0 $$ Where $\alpha$ is the lagrangian multiplier vector. After solving for $w$ (using the dual ...
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Recover $\rho$ of $\nu$-SVM from e1071 package in R

Given a dataset $\{(x_i,y_i)\}_{i=1}^n$, the primal problem for $\nu$-SVM is: \begin{align} &\min_{w,b,\xi,\rho} && \frac{1}{2}w^\top w-\nu\rho+\frac{1}{n}\sum_{i=1}^n\xi_i\\ &\text{...
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Which classification model should I choose and Why?

I am working on a research-based assignment where I suppose to build a 3-class (bad, medium, good) classification using SVM. The dataset provided is imbalanced. The train:test splitting ratio is 75:25 ...
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SVM Classifier with RBF kernel works well with cross validation on training data, but fails on test data. What's going on?

According to the Practical Guide: We propose that beginners try the following procedure first: Transform data to the format of an SVM package Conduct simple scaling on the data Consider the RBF ...
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Is a polynomial kernel ridge regression really equivalent to performing a linear regression on those expanded features?

Say we have a dataset, X, which is Nx2 where N is the number of examples and 2 is the number of dimensions "features". If we were to run a kernel ridge regression (or SVM or whatever) on these ...
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Presenting results of SVM model following LOOCV

I have a small dataset of 60 points and used an SVM regression model (with linear kernel) to train a model to predict $\bf{y}$ from two features, $\bf{x_{1}}$ and $\bf{x_{2}}$. I used LOOCV to provide ...
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How to ensure smoothness in support vector regression

Let's say I have a set $I$ of input parameters, a set $J$ of output parameters, and a set $N$ of experiments that relates $I$ to $J$ for each $n\in N$. When I train the algorithm how can I ensure ...
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How to prove 1-norm radial function is kernel?

How should I prove that is a valid kernel: $K(x,y)=exp(-\alpha||x-y||_1) $ As I understand, there are three ways to prove that prove $K(x,y)=<\phi(x) ,\phi(y) >$ prove $\sum_{j,k=1}^n a_j\,...
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1answer
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Between-subject fMRI classification: subjects with different number of runs

The main purpose of my work is to discriminate patients vs healthy controls using fMRI and multivariate pattern analysis (MVPA). Since I want to classify at the subject level I performed a separate ...
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1answer
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Grid search in r using caret package

I'm using the train function from the caret package to perform a grid search. (data is the spambase dataset from UCI Repository. I've added a row with headers) 1) Is this the way to perform a grid ...
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What's different between hinge loss and squared hinge loss in SVC?

Sorry for the bad english. I'm an Asian. As the title says, What's different between hinge loss and squared hinge loss in SVC and how they effect on the decision boundary? And could you tell me when ...
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Anyway i can improve this multi class classification result?

I am building a multi class classification model using SVM to predict the grade for essays. What can I do to improve the result especially for class 1 and class 3? Their precision and recall are ...
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Why does SVM model need a test set?

First, sorry for the bad english. I'm an Asian so I'm not familiar with english. I'm studying SVC and I understood that a decision boundary of SVC only uses subset data of entire data set, which are ...
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Seperate Data by using hinge loss

It is more a theoretical question: It is given the labels or targets $t\in \{-1,1\}$ and the related prediction $\hat{y} \in [0, \delta_{max}]$. I want to find a threshold $\vartheta \in [0, \...
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Architectures for Text Genre Classification

I am currently trying to build a model for giving genres to news articles. I was wondering what kind of architectures would be good to use for such a task? I am pretty unfamiliar with the current ...
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Can linear SVM classify samples if there is no difference in means of predictors?

Let's say we have a standard classification problem where we want to classify samples into two groups based on some number of predictors. Is it possible to do this with above-chance accuracy, if ...
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LinearSVC hyperparameters Optimization using HyperOpt on python

i am try to optimize a LinearSVC hyperparameter C by using HyperOpt library on python and i don't know which range to put to the C. I am using the loguniform distribution implemented in the HyperOpt ...
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Bilinear Process

how to generate a functional bilinear process such as: $ X_{n+1}= \int\psi(t,s)X_{n}(s)ds + \iint\phi(t,s,u)X_{n}(s)\varepsilon_{n}(u)dsdu + \varepsilon_{n+1}(t) $ ? About the first integral there ...
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How do the kitchen sink approach used to extract Algorithm's feature?

Hi while reading the article of Predicting Unroll Factors Using Supervised Classification of Saman Amarasinghe and al. they mentioned that they used kitchen sink approach for features extraction. ...
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Impact of C on geometric margin in linear SVM

Will the geometric margin always decrease if we increase $C$ in a linear SVM? When data is linearly separable, that makes sense but I can't really see it when we have nonlinearly separable data.
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SVM favouring one of the two classes

I have a binary classification problem (class 1 and class 0). I need to place the hyperplane such that it avoids ...
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23 views

Advantages of dual formulation

Why do we solve the dual form of the SVM in practice to obtain a classifier instead of the primal?
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1answer
77 views

Is the absolute value of the difference a kernel?

In particular is $$ k(x_i,x_j)=|x_i-x_j|, \quad x_i,x_j\in \mathbb{R}$$ a valid kernel?
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1answer
44 views

is permutation testing functionally equivalent to training/test?

If permutation testing is applied in machine learning, is permutation testing functionally equivalent to training/test?
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SVM training yields too many (or no) support vectors

So I implemented a support vector machine, using either a linear kernel or the rbf-kernel. I trained and tested it on a two dimensional set of data and everything seems to be working fine. However, ...
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Why setting SVDD's C-parameter to $> 1$ does not affect the result?

Why setting $C>1$ does not affect the result (compared to $C=1$) according to: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#libsvm_for_svdd_and_finding_the_smallest_sphere_containing_all_data ...
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Nested k-fold cross validation: How to choose hyperparameter for a SVM

I am currently trying to understand how exactly to use nested k-fold cross validation for hyperparameter tuning / model selection. There is one aspect I really cannot get my head around. I found ...
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Final model from nested Cross validation

I use linear SVM and have a small dataset. Because of this I decided to so nestedCV for model checking and dir obtaining the penalty Parameter C. However, I am still confused on how to get to my final ...
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How can I create a meaningful weighting for RMSE?

Background I should start off by saying I am not a mathematician and please excuse simple/stupid mistakes! The goal of my exercise is to find the “best-fitting” model for the purpose of prediction. ...