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."

Filter by
Sorted by
Tagged with
1
vote
1answer
25 views

Using caret::sbf to apply feature selection where features are selected over different threshold scores

I'm aiming to use caret::sbf to filter a large number of predictors before using different machine learning models to predict a binary outcome. I would like to filter for variables that are identified ...
2
votes
0answers
12 views

SVM Scaling problem with One-Class SVM

I'm trying to mess around with a one-class SVM implementation I hacked together from ArduinoSVM. I'm using an RBF kernel and training the model with just "in" datapoints with sklearn. First, as is ...
7
votes
3answers
930 views

What happens in the sub-areas of AI? (ML, DL)

I have problems with understanding the sub-areas of AI and how it works. AI has the sub-area Machine Learning (ML), in which learning algorithms are used. Supervised/unsupervised learning takes place ...
1
vote
0answers
25 views

Using support vector regression to estimate GARCH model

I would like to estimate GARCH (1,1) model with support vector regression and I want to use asset returns series. I want to use python. Can anybody help me to explain the process with example step-by-...
1
vote
0answers
22 views

can a linear classifier based on two variables separate a dummy variable?

i have this problem : as you can see, on the x-axis there is the variable age and on y-axis there is the wkswrkd which is ...
0
votes
0answers
8 views

Margin of boundary decision

I'm learning margin, decision boundary for linear classifer for binary classification. Let's have a hyperplan $$ \langle \theta, x \rangle + \theta_{0} = 0 $$ and we consider the margin $\gamma$ ...
0
votes
0answers
18 views

Trying to plot a dummy variable over a dataset

Hy everyone, im new to ds, so please,be kind. i've this dataset ...
0
votes
1answer
12 views

Clarification on SVM

I have a data set with 7 observations and 2 dimensions. I have gone ahead and calculated the equation for the maximal marginal hyperplane which was relaying on 4 points, say z1,z2,z3, and z4. These ...
0
votes
1answer
12 views

Decision function and predict for svm binary classifier

I am plotting decision region and prediction for svm but both are contradicting ...
0
votes
0answers
11 views

can we normalize text data in machine learning using python

i have developed a application for text classification ,it is taking to much time to execute support vector machine due to normalization issue,so that i am asking ,can we normalize the text data ...
0
votes
1answer
13 views

Do non-linear Support Vector Machines need data to be scaled with zero mean and unit std?

I am trying to understand the difference between standardisation, normalisation and scaling for features. My data roughly follows a bell shaped/Gaussian distribution but the magnitude of their ...
1
vote
0answers
10 views

Can someone explain how a kernel trick makes things computationally easier?

This author states that when we use the kernel trick, we map original features to the distance between two values. I have two questions regarding this: I'm not seeing how this is computationally ...
0
votes
0answers
8 views

How to select the best features for Support Vector Classifier in sklearn

I have a range of different technical analysis indicators as a feature set for my SVM. I would like to think some indicators are better than others at predicting and that there must be some sort of ...
0
votes
0answers
13 views

White box/ interpretable model

I am looking for classification/ regression methods that could be considered "white box" models, meaning they have some degree of interpretability or "can be explained". My dataset has 16 dimensions ...
0
votes
0answers
18 views

Can someone explain to me in layman's terms the difference between a dot product and inner product? [migrated]

I am reading this and it's not exactly clearing things up for me: https://math.stackexchange.com/questions/476738/difference-between-dot-product-and-inner-product The only thing I got from searching ...
0
votes
0answers
15 views

Calculate expected gradient length in one-vs-rest or one-vs-one scenario with SVM

According to the paper "Active learning with support vector machines" (LINK) by Kremer et al. from 2014 it is possible to apply the expected gradient length on Support Vector Machines (SVM). Is it ...
1
vote
0answers
23 views

Minimization Problem Involving Kernels

Let $f(x) = \sum_{i=1}^n \alpha_ik(x,x_i) + \alpha_0$ for some kernel $k$. I want to show that there exists a kernel such that the solutions to the following optimization problems are the same: $$\...
0
votes
1answer
29 views

Why do we specify a soft margin in Support Vector Machines?

If we're interested in classifying, we only need the decision boundary. I know I'm missing something. Is soft margin the smallest length on both sides of the decision boundary such that all ...
0
votes
0answers
12 views

OCSVM hyper parameters tunning for anomaly detection

I am implementing an One Class Support Vector Machine (OC-SVM) for an anomaly detection module. What I would like to do now is to find the best hyperparameters (nu, gamma, kernel) for my specific case....
0
votes
0answers
7 views

Simultaneous optimization of model parameters and number of features in classification

I am using a linear SVM classifier for binary classification problem in a small dataset (60 samples total, balanced i.e. 30+30). My features are 60 in number. I am using an aggregate of filter methods ...
0
votes
0answers
20 views

Creating final machine learning model: how many times to train?

I am using a nested cross-validation framework where I optimize the number of features and box constraint parameter for linear SVM using grid search in the inner CV split and assess how good the model ...
1
vote
1answer
28 views

SVM overfitting

I'm currently using a SVM (classification) to predict the outcome of a sports match. I split the data into three sets, a training, cross-validation, and test set. I have a total of 2200 sample points. ...
0
votes
1answer
39 views

Using caret::sbf to apply feature selection and classification

I'm aiming to use caret::sbf to filter a large number of predictors before using different machine learning models to predict a binary outcome. I would also like to optimise tuning parameters and do ...
0
votes
0answers
14 views

Question about SVM Decision Boundary Perpendicular to Theta Vector

In Dr. Andrew Ng's explanation of support vector machine in "Mathematics Behind Large Margin Classification", at around time 15:25, Dr. Ng mentions that the vector $\theta$ is perpendicular to the ...
0
votes
0answers
13 views

Question about SVM Large Margin Intuition

I have a question about support vector machines. I was watching Andrew Ng's lecture on "Large Margin Intuition". Starting from 0:18 of the video, he says the following: If $ y = 1$, we want $\theta^...
3
votes
1answer
42 views

Why can we do this in SVM optimization?

I posted this question in the datascience stackexchange but no one answered so I thought I would post it here aswell: Ok, so I've been trying to read up on how SVM:s work and started with maximal ...
1
vote
0answers
21 views

Understand the support vector regression formulas

So I am trying to get the SVR boundary conditions. w normal vector of the plain x feature vector ...
1
vote
2answers
22 views

hyperparameters optimisation with linear kernel

I want to conduct an SVM model-regression (i.e., support vector regression), using a linear kernel function. Does it make sense to perform a cross-validation hyperparameter optimization when the ...
0
votes
1answer
31 views

Classifying 10 top features using SVM

I'm new in R. I have a problem with classification using SVM. In my train_data, I have 82 samples and 25770 features while in my test_data I have 36 samples and 25770 features. The last column is the ...
2
votes
1answer
21 views

LibSVM - interpreting model output

I am using libSVM on a subset of the MNIST, and I am struggling to interpret the output. I have learned that rho is the bias term, and that sv_coef is the multiplier used to get to the weight term. ...
0
votes
0answers
9 views

Feature Selection by individual AUC

I am creating a model for classification and I have several ways to get subset of features but I was wondering if the following is reasonable: Use the train set to calculate LOOCV or LPOCV AUC values ...
1
vote
1answer
76 views

Calculating the value of $b^{*}$ in an SVM

In Andrew Ng's notes on SVMs, he claims that once we solve the dual problem and get $\alpha^*$ we can calculate $w^*$ and consequently calculate $b^*$ from the primal to get equation (11) (see notes) ...
0
votes
1answer
27 views

Feature Selection - Overfit?

I have a dataset of 100 patients and 1500 features. I split 80 train 20 test first and then use the train set to get the best hyperparameters / best feature subset doing the following: I randomly ...
1
vote
2answers
28 views

Confusion about Karush-Kuhn-Tucker conditions in SVM derivation

I am currently following CS229 and I'm trying to be diligent, proving most of the things that are not immediately obvious. So I'm looking at the derivation of the SVM as the dual problem to the ...
2
votes
1answer
35 views

Interpretation of C hyperparameter in support vector classifier?

I found a contradiction between inteprpetation given by university book "Introduction to Statistical Learning" and all the other web sources. In the first one it is said that the larger is C the wider ...
0
votes
1answer
35 views

SVM: Getting number of support vectors number and relationship between C and alpha in Python sklearn SGDClassifier

I am using sklearn.SGDClassifier to train my SVM model with loss='hinge'. My questions are: Is there a way to get support vectors number by having this SGD model? I found this online but it is not ...
0
votes
0answers
17 views

Relation between Bayesian Linear Regression (Fixed base: Gaussian RBF) and SVM RBF?

I am trying to get my head around Bayesian Linear Regression. I am looking at a Gaussian radial basis function, which I assume acts as our prior. I have the following diagram: My current ...
0
votes
0answers
17 views

Organizing Data for hourly and daily predictions

Let's suppose I'm using SVM (Regression) to predict variable y and I have multiple input variables (x_i) which are data from sensors at intervals of 10 minutes. From an operational point of view, I ...
1
vote
0answers
15 views

What is the decision function used in sklearn's SVC SVM

When I am using sklearn.svm.SVC I have set the decision function to 'ovr', but am struggling to find the exact decision function equation at the moment. Can anyone help me out? Thanks in advance.
0
votes
1answer
24 views

Cannot use SVM with RBF Kernel

I'm new in R. I have an original dataset with 25771 variables and 118 samples. I already performed feature selection and split the dataset into 70 30 so i have 82 samples in my training data and 36 in ...
1
vote
1answer
25 views

Precision and recall for SVM from Confusion matrix is different from Precision-Recall graph

Coming from Stackoverflow- So, I am creating a SVM model for a highly imbalanced data set and trying to create to calculate F, Pression and recall from the confusion matrix of the model. Confusion ...
2
votes
1answer
33 views

Can non-linearly separable data always be made linearly separable?

A data set that is linearly separable is a precondition for algorithms like the perceptron to converge. It's well-known that we can project low-dimensional data to a higher dimension using kernel ...
0
votes
1answer
12 views

How to convert SVM values into corresponding class type?

I am doing SVM on R, and when I do: ...
1
vote
1answer
19 views

Kernelized perceptron algorithm weights update

I'm asked to find the maximum margin decision surface separating positive from negative samples by inspection. The positive examples are (1,1) and (-1,-1), the negative ones are (1,-1) and (-1,1). The ...
1
vote
2answers
57 views

Why does the supporting vector satisfy $y_i(\mathbf{w}^T\mathbf{x_i}+b) = 1$ instead of $> 1$ or $= 2$

The SVM is about solving the constrained optimization such that $$\min_{\mathbf{w}} \dfrac{1}{2} \mathbf{w}^T\mathbf{w}$$ subject to $$y_i(\mathbf{w}^T\mathbf{x_i}+b)\geq{1}, i=1, 2, ...,n$$ ...
0
votes
0answers
8 views

Why do Support Vector Data Description and One Class Support Vector Machine produce the same results?

Quoating from Chapter 5 of Kernel Methods in Computer Vision by Christoph H. Lampert 'A quick geometric check shows that if all data points have the same feature space norm and can be separated ...
0
votes
0answers
18 views

Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
2
votes
2answers
34 views

Optimization equivalence

Can someone help me with the step by step demonstration of the following equivalence used in SVM: $$maximize: m = \frac{1} {\|w\|} \equiv minimize: m =\frac{1} {2}\|w\|^2 $$ I would be most grateful ...
2
votes
1answer
28 views

Why the weight vector is a linear combination of the inputs and the outputs in the Perceptron

I was studying Support Vector Machines and I've got stuck with this relation regarding the weight vector of the hyperplane. $w=\sum\limits_{i\in I}^{} y_i x_i$ For reference, I'm studying from the ...
1
vote
0answers
18 views

Dimensions of feature transfrom $\phi(x)$ for a kernel Support Vector Machines

Given a kernel function $K(x, x') = \langle \phi(x), \phi(x') \rangle$, how can we figure out the dimensions of the feature transform $\phi(x)$? For example, for $K(x, x') = (1+x^Tx')^M$

1
2 3 4 5
40