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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questions
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11 views
Pruning of SVM classifier [closed]
I am trying to perform the pruning technique for SVM classifier.
I couldn't find any python code examples for SVM pruning to get inspired.
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15 views
Image Classification using Single Class Dataset using One Class SVM [closed]
I have the dataset of vehicle images, about 1000 images. However, I do not have any non-vehicle images as it could be any besides vehicle. I am trying to train a model with single class dataset and ...
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1answer
51 views
A generalized quadratic loss and Newton iteration for SVM, why doesn't it generalize well? [closed]
I'm comparing the results of an Newton optimizer for a modified version of SVM ( a generalized quadratic loss, similar to the one stated in:
A generalized quadratic loss for SVM
) with classic SVM^...
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3answers
212 views
ROC curve interpretation
I trained a CNN model and a combined CNN-SVM model for classification. I wanted to compare their performance using ROC curve but I was confused which model is better. How to interpret the given ROC ...
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1answer
41 views
How can different classification algorithms expressed as neural networks?
I have heard that each of the different classification algorithms can be expressed as a neural network architecture. How can the different algorithms like Logistic Regression, SVM(Support Vector ...
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2answers
94 views
What is the difference between LS-SVM and P-SVM?
What is the difference between least-squares SVM (LS-SVM) and proximal SVM (P-SVM)? How does the decision boundary change in case of both of these types of SVM?
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1answer
34 views
Genetic algorithms: what connection to support vector machine / naive bayes
I found the following list of seven classifiers:
Linear Classifiers: Logistic Regression, Naive Bayes Classifier
Nearest Neighbor
Support Vector Machines
Decision Trees
Boosted Trees
Random ...
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0answers
5 views
SVM, which range to use when normalizing
I am using the SVM classifier from Scikit Learn.
I was wondering is there is a know-best-practice when it comes to normalization. I'm using different normalization tecniques, but all my normalized ...
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0answers
10 views
Machine learning algorithm for classifying a 2xN array of ranged coordinates?
Good afternoon,
I have a dataset of lists of coordinates that are ranged from (0, 100) on the Y-axis and (0, 300) on the x-axis, with double precision. I'm looking into classifier algorithms that ...
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0answers
16 views
In SVM, is the support set still small if kernel trick is used?
In SVM, we classify y based on whether f(x) > 0 or f(x) < 0.
I understand that in SVM with f(x) being linear in x, the support set is typically small (i.e., the number of support vectors is much ...
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2answers
66 views
Which scoring for GridSearchCV is best, when imbalanced multiclass dataset?
I have an unbalanced multiclass dataset (GTSRB) and want to optimize the hyperparameters of an SVM through GridSearchCV.
I know that accuracy is not suitable for scoring in this case.
Which evaluation ...
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0answers
8 views
How can I classify single fused gray scale image in python ? is it possible to binary classsify single output image with Ground Truth image?
I want to calculate the precision, recall, and accuracy of the single predicted image(y_pred) with the Ground truth (y_true) image. I have only two binary class (0 and 1) so my question is, **
is it ...
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0answers
16 views
Do not scale Hog features?
when I train LinearSVC with the Hog features extracted from the Fashion-MNIST dataset then I get better results if I don't use StandardScaler before training than
I use it.
...
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1answer
38 views
K-fold-cross-validation if training dataset is much smaller than test dataset?
I'm a beginner in machine learning and I have a special case in which I have only a small training dataset of about 500 images and a test dataset of 10,000 images.
Does it still make sense to do a 10-...
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0answers
15 views
Feature importance in SVM
Why is there no command for feature importance in SVM like the one provided in Random Forest feature_importance_ from ...
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0answers
23 views
How can I map the sample from the original feature space to the new kernel feature space? (Sk-learn)
Let's say I have a very basic SVM model, implementin sk-learn:
clf = SVC(kernel='rbf', class_weight=weights, gamma=gamma)
clf.fit(X,y)
X is the sample space with ...
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1answer
39 views
Non-Convex Constraints for Classification Problems
I am willing to create a hypothetical non-convex constraints for the purpose of practising nonlinear classification using an algorithm. I thought of such constraints in the form: $x^TAx + Bx \leq c$.
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9 views
How to upload a sklearn SVM model as a chrome extnesion?
I have trained an SVM/Logistic regression machine learning model using its scikit implementation.
But now I want to do the same with Tensorflow/Keras. This is for easy conversion to Tensorflow.js. ...
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1answer
32 views
How to deploy machine learning models as a chrome extension?
I have trained a stance detection model using SVMs. Wanted to know how can I deploy this as a chrome extensions. I do understand that the question is a bit broad but any links, suggestions etc. will ...
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1answer
31 views
Implicit feature selection
I have heard that Random Forest and other tree based machines apply some kind of implicit feature selection.
My Question is: Does this also apply for machines like the SVM? As far as I understand is ...
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1answer
30 views
(Scikit-learn) differences between LinearSVC, 'linear' kernel SVC and poly kernel SVC with degree 1
I would like to know the differences between:
linearSVC()
SVC(kernel='lineaer)
...
1
vote
1answer
34 views
SVM hyperplane margin
so that $H_0$ is equidistant from $H_1$ and $H_2$.
However, here the variable $\delta$ is not necessary. So we can set $\delta=1$ to simplify the problem.
$$w\cdot x+b=1 $$
and
$$w\...
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0answers
10 views
Understanding the ||w|| = 1 constraint for SVMs
Is it correct to say that the reason why ||w|| is set to 1 in the formula for the geometric margin is that it then is the sane as the functional margin (i. e. gives the same information) why still ...
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0answers
37 views
Difference between C and lambda in SVM
I've been taking the coursera machine learning course and The instructor said that if ( C = 1/lambda ) then the learning algorithm would reach the same optimal value of theta
does this mean that ...
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2answers
190 views
Binary classfication vs One-class classification
Why do we need samples of both classes for the training of binary classification algorithms, if one-class algorithms can do the job with only samples from one class?
I know that one-class algorithms (...
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0answers
14 views
How to obtain the predictions of SVM model on single input?
So, I am trying to build a Spam detection model. It is trained on a dataset consisting of about 3500 messages. I used SVM to build a model. But, if I now wish to find out whether a message is spam or ...
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1answer
35 views
Sliding window approach using SVR & LightGBM
I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
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2answers
47 views
Why use deep neural networks over methods like linear regression or SVM?
This is a very broad question, but I was wondering why researchers would choose a deep neural network over linear regression or SVM? As in, what are the advantages and disadvantages of both?
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0answers
8 views
Soft Margin SVM kernels
Kernels are used to map datasets into higher dimensions so that they could be linearly separable. However, if we introduce the slack variable in the soft margin SVM, we are allowing some mistakes, and ...
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0answers
10 views
Support Vector Machine (SVM) kernels
I learned that Kernels in SVMs are used to map the datasets into a higher dimension to make it more linearly separable, and the kernels will produce only the result, so we don't even have to know what ...
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1answer
30 views
How Linear SVM Regression and Multiple Linear Regression different in terms of the regression result?
They starts from the same equation as below.
y = w*x + b
But they solve it differently. MLR specified the w and b by minimizing the square error whereas SVM specified w and b by minimizing the loss ...
1
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1answer
154 views
svm.LinearSVC: larger max_iter number doesn't always increase the accuracy/precision/recall
Background:
Supervised machine learning
Data shape
10+ features, target = 1 or 0 only, 100,000+ samples (so should be no issue of over-sampling)
80% training, 20% testing
train_test_split(X_train, ...
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2answers
45 views
SVM Loss Function
I have learned that the hypothesis function for SVMs is predicting y=1 if transpose(w)xi + b>=0 and y=-1 otherwise. However, according to the loss function above, it implies that transpose(w)xi + b ...
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13 views
Quadratic programming and Lagrange multiplier in SVM
I am a little confused because for some simple functions and constraints, using the Lagrange multiplier will be able to solve for the variables. However, in the SVM Lagrange expression, I learned that ...
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16 views
About SVM cost function and Lagrange
I just watched the MIT video on the intuition and mathematics behind SVMs, and overall, I learned that the objective is to minimize the margin or the distance between the support vectors with some ...
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1answer
57 views
Why my svm.SVC.fit( ) (linear kernal) run so long time?
I am using sklearn.svm.SVC( ) to train & test my dataset. 80% are used for training, 20% are used for testing.
Here is my Python code:
...
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0answers
7 views
Implementing Linear SVM with One-sided Soft Margin
In my use case, I have two sets of datapoints $X$ and $Y$ which we want to "approximately" linearly separate. These sets represent the entire population, so I'm not concerned about generalization. In ...
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1answer
26 views
Feature addition/ subtraction and SVM model accuracy
I am working on a text classification problem where I would like to improve the accuracy of my model. Presently, I am using SVM with linear SVC and OneVsRestClassifier. The model should correctly ...
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0answers
27 views
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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0answers
11 views
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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1answer
23 views
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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0answers
15 views
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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0answers
14 views
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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1answer
15 views
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}$$
and ...
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0answers
33 views
SVM - Shuffle image data before GridSearchCV or not?
I have different image datasets, most of them are sorted by class, others are already mixed.
For each of these data sets, I would like to train one SVM (in Python with Scikit-Learn), whereby in each ...
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0answers
18 views
Similarity of perceptron criterion and SVM
In the book "Neural Networks and Deep Learning" by Aggarwal there is an exercise 2.10.1:
Consider the following loss function for training pair $(\overline{X},y)$:
$$L=max(0, a -y(\overline{W} \...
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2answers
42 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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0answers
50 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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0answers
66 views
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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0answers
130 views
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. ...
