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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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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, ...
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Training error higher than test error and validation error

I am training a genetic algorithm for classification and strangely, the training error is consistently HIGHER than the validation and test error. The training and validation set are both small size ...
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How effective is SVM over big datasets?

I have a dataset of 800,000 observations and 11 features that I am using for a classification problem. I tried to optimize my model many times but in vain. The one thing I haven't tried is using SVM. ...
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Is it a good idea to use CNN to classify 1D signal?

I am working on the sleep stage classification. I read some research articles about this topic many of them used SVM or ensemble method. Is it a good idea to use convolutional neural network to ...
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Getting Realistic Test Metric With Overlapping Train and Test Data

My question is a rather specific one, and I'll go into detail below, but basically boils down to this: Are there any ways to get realistic test accuracy for an SVM classifier fit on data that ...
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WoE for Random Forest and SVM

There are a lot written about WoE (Weight of Evidence) transformation for the case of Logistic Regression Classifier. It works great. The question: can one (or does it make sense) to use this WoE ...
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1answer
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Modelling small data set problem

I have a small dataset (20 instances per 13 classes). The 13 classes are human users from their behavior features, I have to classify if an unseen behavior feature is of a user or not. Data: These ...
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How to tune the hyperparameters for oneclass SVM while doing unsupervised learning?

For my task, I am doing unsupervised learning and I am trying to find the best possible value of the parameters gamma and nu in ...
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SVM test data point classification

I am new to machine learning. What if a data point falls inside the support vector margin? Below wx-b = 1 and above wx-b = 0 OR Above wx-b = -1 and below wx-b = 0.
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Why do we get contour around dataset when we use rbf kernel in svm?

Why do we get contour around dataset when we use rbf kernel in svm? For example, we use kernel trick to map 2D data into 3D and we can use linear hyperplane to split data, but when we get back to 2D ...
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The default output of Matlab fitcsvm function for SVM classifier

I would like to know the default model of 'fitcsvm' function, does it considered soft linear model? if so, how can I build a hard margin SVM model in matlab?
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Optimal separating hyperplane for SVM

So I was reading the math behind SVM and the way the optimisation problem is posed. My question is why does the optimal separating hyperplane always have to be equidistant from the two marginal ...
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What would be the biggest considerations in using an SVM for NLP?

Evaluating a linear SVM on an NLP corpus where there are 150,000 data examples but each language sample is reasonably short(10-15 words). This is evaluated against a code that is a topic. For example "...
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Still Overfitting SVM with Cross-Validation and Grid Search

I am relatively new to machine learning and am trying to implement an SVM for the first time on a project, but I'm running into some overfitting-related issues. Basically, I created a function called ...
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1answer
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how this transform 2d to 3d is work in mathmatically?

this is the 2d->3d projection for svm. they used the kernel trick to change the dimension of the vector for easier classification. I want to understand the detail math behind this projectin which ...
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How to calculate if the given dataset is linearly separable or not using the given discriminant function [duplicate]

I am student and learning SVM with radial basis kernel function and want to solve the question manually by hand. but I am unable to calculate the values to find out whether the dataset is linearly ...
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How to manually calculate if the given dataset is linearly separable or not using the given discriminant function

I am student and learning SVM with radial basis kernel function and want to solve the question manually by hand. but I am unable to calculate the values to find out whether the dataset is linearly ...
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1answer
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svm's margin equation derive question

I hava question about the margin equation $$\frac{a}{||w||}$$ where this equation coming from? I think it substract the $w^{T} +b -a - w^{T}x +b$ but not sure how margin equation derived
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disadvantages of svm

since I was reading about disadvantages of svm(support vector machine) Non-Probabilistic - Since the classifier works by placing objects above and below a classifying hyperplane, there is no ...
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Python: How to find the value that separates 2 different clusters?

I am applying an unsupervised learning algorithm for building an anomaly detection using OneClass SVM method and then plotted it to visualize how it looks. I got 2 clusters: one red and the other ...
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increase dimensionality svm example

I understood that increasing the dimensionality in SVM will help, but I try to understand the concept mathematically, as described in Breiman, Leo. "Statistical modeling: The two cultures." ...
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Adaboost Training Error and It's Trend

The Adaboost M1 algorithm is as follows: $\mathbf{Input}$: sequence of m examples $<(x_1,y_1),...,(x_m,y_m)>$ with the labels $y_i \in Y = \{1,...,k\}$ weak learning algorithm WeakLearn ...
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What would be the “Leo Breiman style” alternative to Platt scaling?

In Platt scaling[1], you fit a function to the predicted raw scores from a trained model, on a test set, in order to convert future scores into probabilities. The function is: p(x) = 1 / (1 + exp(a*x ...
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Geometrical interpretation for a one class svm

I encountered the following question on a test exam: For an unsupervised dataset $X = \{x_1, \dots, x_n\}$ we would like to solve the optimization problem: $$ min_w ||w||^2$$ subject to $$w^T ...
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Why the score of validation curve is a constant at the beginning and finally?

The sample size is 105 (69 samples about label 0 and 36 samples about label 1) and it contains 10 features. After scaling feature to [0,1] by MinMaxScaler(), I use svc ('rbf'kernel) and 5-fold cross ...
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Optical Character Recognition - digits on the screen

My task is to classify a digit based on a small image containing one digit only. The font type and size is the same across the training/test dataset, but the position of the digit in the image might ...
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Interpretation of Feature Importance Statistic

I would like to discuss the managerial implications of the outcomes of a support vector machine model. I assessed the feature importance statistic for the support vector model (package ‘rminer’) and ...
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Application of Recursive Feature Elimination to Support Vector Machine Model

I have developed a support vector machine model with 78 features on which I would like to apply recursive feature elimination. I developed the model with the package ‘rminer’. fitmodel<-fit(...
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Help: Null Values for penalized SVM

I am interested in fitting a SVM model to my data with Elastic SCAD penalty. I was trying to use the penalizedSVM library for this. The issue is that for some reason, the library outputs a null model ...
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Does High Dimensional Data effects SVM?

As we move into higher dimensions, we will find even more corners. This will make an ever increasing percentage of the total space available. Now imagine we have data spread across some ...
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1answer
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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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1answer
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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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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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1answer
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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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1answer
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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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1answer
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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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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
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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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2answers
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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 ...