Questions tagged [support-vector-machine]
For questions about support vector machines (SVMs), which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
59
questions
-2
votes
0
answers
8
views
Can i integrate yolov5 with svm algorithm
I am currently working on an object detection project. I have already done it with yolov5 and svm separately can i integrate both the algorithm together like YOLO for object localization and detection ...
3
votes
1
answer
155
views
How do I poison an SVM with manifold regularization?
I'm working on Adversarial Machine Learning, and have read multiple papers on this topic, some of them are mentioned as follows:
Poisoning Attacks on SVMs: https://arxiv.org/pdf/1206.6389.pdf
...
CommunityBot
- 1
1
vote
0
answers
17
views
Why is the Hinge Loss defined this way?
I have a question regarding the Hinge Loss function used for classifiers and in general the "max-margin" types of classifiers, it is defined as $$max(0,1-t*y)$$ where $t$ is the intended ...
- 111
0
votes
0
answers
14
views
Why are projected variables in canonical correlation analysis uncorrelated?
Let $x\in R^d$ and $y\in R^e$ be two vectors with covariance and cross-covariance matrices
$S_{xx}, S_{yy}, S_{xy}, S_{yx}$. The canonical correlation analysis is based on the projection of $x$ onto ...
- 492
0
votes
0
answers
17
views
Can we derive the support vector machines dual formulation without directly using lagrangian duality theory?
Lagrangian duality theory allows us to derive the dual formulation for support vector machines and to show that the primal and the dual solutions are equivalent.
My question is: is it possible to ...
- 1
0
votes
1
answer
142
views
Machine learning for arranging 2D points
I have a problem wherein I have 2D points in an image that would be associated with a corresponding label/sequence number. For instance following are 4 such examples:
As you can see all of them have ...
CommunityBot
- 1
7
votes
2
answers
804
views
How should we interpret this figure that relates the perceptron criterion and the hinge loss?
I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following:
The perceptron criterion is ...
CommunityBot
- 1
0
votes
0
answers
16
views
Meaning of Objective and Risk in DLIB HOG-SVM detector
I am using dlib simple object detector for training a HOG-SVM object detector. Everything is working fine when I test manually. However, I can't find any resources that tell me what is the meaning of ...
0
votes
0
answers
16
views
My text classifier behaves like regex
I'm trying to train binary classifier that classifies ask to ask programming questions, programming questions that say "I'm getting an error about x/I have problem about x" but don't say the ...
0
votes
0
answers
16
views
Intuition behind replacing constraint in equation for Optimal Separating Hyperplane
I am reading "Optimal Separating Hyperplane" section of the book - Elements of Statistical Learning which is described on page 132 as follows:
My questions:
The constraint $||\beta|| = 1$ ...
0
votes
0
answers
19
views
What happens if one uses non-valid kernels in regression?
When introducing kernelization in regression, it is emphasized that a kernel function $k(x_i, x_j)$ has to represent a scalar product in some high dimensional feature space, $k(x_i, x_j) = \phi(x_i)^T ...
- 101
0
votes
0
answers
22
views
How would I encode a variable-length array to use an SVM?
I'm working on some image processing, and I have a list of contours
(it's essentially a list [or array] of (X, Y) coordinate pairs). These vary in length, depending on the size of the found contours.
...
- 101
1
vote
1
answer
288
views
How do you perform a gradient based adversarial attack on an SVM based model?
I have an SVM currently and want to perform a gradient based attack on it similar to FGSM discussed in Explaining And Harnessing
Adversarial Examples.
I am struggling to actually calculate the ...
- 276
5
votes
1
answer
387
views
Do Support Vector Machines have the ability to learn while in use?
I've read in some literature,that SVMs are characterized by their adaptivity. Does that mean they can learn while in use?
- 1,094
0
votes
1
answer
137
views
What to say about time complexity of SVM?
I've read it in some literature now that the training speed of SVM (in general) is very low. Why is that the case? What is to say about time complexity of SVM?
CommunityBot
- 1
0
votes
0
answers
82
views
How to use PCA to reduce the dimension of output features of convolutional layers of CNN
I am working on a hybrid CNN-SVM classification using python. I tried to get the final output features of the CNN model(flatten layer) to be fed to the SVM classifier. The output shape obtained from ...
- 11
1
vote
1
answer
167
views
Why are SVMs / Softmax classifiers considered linear while neural networks are non-linear?
My understanding is that neural networks are definitely not linear classifiers, as the point of functions like ReLU is to introduce non-linearity.
However, here's where my understanding starts to ...
- 151
1
vote
0
answers
69
views
Why does the SVM perform poorly on test data that has a different class distribution than the training data?
Do you know why the SVM performs poorly on test data that has a different class distribution than the training data? The training data has around 15 classes, and the additional testing data has around ...
- 11
0
votes
1
answer
39
views
Distinguishing text with opposite meanings in SVM (False Information Detection)
I am currently working on a Binary Text Classification Model (False Information Detection) using Support Vector Machine and used TF-IDF as text vectorizer in Python. I have already tried training the ...
- 5,218
0
votes
1
answer
66
views
Does $(\langle w, x \rangle + b) = ||x - x'||$ hold?
Currently, I am trying to understand the mathematics of SVM's using the textbook 'Mathematics for Machine Learning' by Deisenrot et. al.
On page 375, they define the distance between a point $x$ and ...
- 5,218
1
vote
1
answer
170
views
What is the definition of the "cost" function in the SVM's objective function?
In a course that I am attending, the cost function of a support vector machine is given by
$$J(\theta)=\sum_{i=1}^{m} y^{(i)} \operatorname{cost}_{1}\left(\theta^{T} x^{(i)}\right)+\left(1-y^{(i)}\...
- 39.5k
0
votes
1
answer
114
views
Do I need to tune the hyper-parameters or more data if SVR model performs poorly?
I am using non-linear data to SVR and have tried tuning the hyperparameters and still have a poor model performance. Do I need more data or format the data for more suitable results?
I get similar ...
- 3,612
10
votes
7
answers
34k
views
Why does training an SVM take so long? How can I speed it up?
I'm trying to create and test non-linear SVMs with various kernels (RBF, Sigmoid, Polynomial) in scikit-learn, to create a model which can classify anomalies and benign behaviors.
My dataset includes ...
- 2,564
0
votes
0
answers
70
views
How can I weight each point in one-class SVM?
I want to give weights to some data points
Specifically, these are points related to anomalies
(I'm implementing one-class SVM for anomaly detection)
Exactly, I want to consider some data points that ...
4
votes
2
answers
135
views
Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD?
In a final project in diagnosing Attention deficit hyperactivity disorder (ADHD) using Machine Learning, we obtained parameters from real patients. We used this data and got much higher success rates ...
- 39.5k
1
vote
2
answers
394
views
If the training data are linearly separable, which of the following $L(w)$ has less optimum answer for $w$, when $y = w^Tx$?
I'm studying machine learning and I came into a challenging question.
The answer is 2. But based on my ML notes, all of them are true. Where are the wrong points?
CommunityBot
- 1
3
votes
1
answer
167
views
Why do we use the word "kernel" in the expression "Gaussian kernel"?
I've heard the expression "Gaussian kernel" in several contexts (e.g. in the kernel trick used in SVM). A Gaussian kernel usually refers to a Gaussian function (that is, a function similar to the ...
CommunityBot
- 1
4
votes
1
answer
840
views
How to implement SVM algorithm from scratch in a programming language?
I'm a computer scientist who's studying support vector machines (SVMs) in a machine learning course. I have some understanding of how SVMs are designed, thanks to 16. Learning: Support Vector Machines ...
CommunityBot
- 1
1
vote
1
answer
418
views
Is my 57% sports betting accuracy correct?
I have been creating sports betting algorithms for many years using Microsoft access and I am transitioning to the ML world and trying to get a grasp on determining the success of my algorithms. I ...
CommunityBot
- 1
1
vote
0
answers
22
views
Can you correlate decision boundary of final layer of a neural network to predictive distribution?
I was reading in a On the Decision Boundary of Deep Neural Networks
that the final layer of a MLP can be equated to an SVM and can generate decision boundaries similar to methods with SVM. I was ...
- 737
1
vote
1
answer
327
views
Is an SVM the same as a neural network without a hidden layer?
A neural network without a hidden layer is the same as just linear regression.
If I then use squared hinge loss and encoporate the l2 regularisation term, is it fair to then call this network the ...
- 39.5k
2
votes
1
answer
6k
views
What is the definition of the hinge loss function?
I came across the hinge loss function for training a neural network model, but I did not know the analytical form for the same.
I can write the mean squared error loss function (which is more often ...
- 39.5k
2
votes
1
answer
123
views
Why doesn't the set $\{ -2, +2 \}$ in $E(X) = (y − \text{sign}\{\overline{W} \cdot \overline{X} \}) \in \{ −2, +2 \}$ include $0$?
I am currently studying the textbook Neural Networks and Deep Learning by Charu C. Aggarwal. Chapter 1.2.1.2 Relationship with Support Vector Machines says the following:
The perceptron criterion is ...
- 547
1
vote
1
answer
518
views
What is the smoothness assumption in SVMs?
In this research paper, we have the following claim
the smoothness assumption that underlies many kernel methods such as Support Vector Machines (SVMs) does not hold for deep neural networks trained ...
- 36
13
votes
2
answers
798
views
Are the shortcomings of neural networks diminishing?
Having worked with neural networks for about half a year, I have experienced first-hand what are often claimed as their main disadvantages, i.e. overfitting and getting stuck in local minima. However, ...
- 39.5k
3
votes
0
answers
89
views
Why is the margin attained with $\Phi=\left[2 x, 2 x^{2}\right]^{T}$ greater than the margin attained with $\Phi=\left[x, x^{2}\right]^{T}$?
I am trying to understand the solution to part 4 of problem 3 from the midterm exam 6.867 Machine learning: Mid-term exam (October 15, 2003).
For reproducibility, here is problem 3.
We consider here ...
- 31
2
votes
1
answer
236
views
What are support values in a support vector machine?
I started reading up on SVM and very little is defined of what are support values. I reckon it's they are denoted as $\alpha$ in most formulations.
- 39.5k
2
votes
0
answers
61
views
How does the support vector machine constraint imply that sample selection bias will not systematically affect the output of the optimisation?
I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3.4. Support vector machines, the author says the following:
3.4. ...
- 547
1
vote
1
answer
66
views
Does the image is logistic regression or SVM, and why? [closed]
Does the image is logistic regression or SVM, and why?
- 121
2
votes
1
answer
160
views
Support Vector Machine Convert optimisation problem from argmax to argmin
I'm new to the AI Stackexchange and wasn't certain if this should go here or to Maths instead but thought the context with ML may be useful to understand my problem. I hope posting this question here ...
- 21
1
vote
1
answer
157
views
Why does the training time of SVMs dramatically decrease after applying dimensionality reduction to the features?
Training an SVM with an RBF kernel model with c = 5.5 and gamma = 1.06, for a 5-class classification problem on the NSL-KDD ...
- 39.5k
2
votes
1
answer
151
views
Does the bag-of-visual-words method improve the classification accuracy?
I'm a beginner in computer vision. I want to know which option among the following two can get better accuracy of image classification.
SIFT features + SVM
Bag-of-visual-words features + SVM
Here's ...
- 793
5
votes
1
answer
667
views
How do I use a taxonomy and the support vector machine for question classification?
I am going to develop an open-domain natural language question-answering (NLQA) system, and will use the support vector machine (SVM) as the machine learning (ML) model for question classification.
...
- 39.5k
2
votes
2
answers
3k
views
What is the purpose of the "gamma" parameter in SVMs?
I want to understand what the gamma parameter does in an SVM. According to this page.
Intuitively, the gamma parameter defines ...
- 39.5k
1
vote
1
answer
82
views
How to understand mapping function of kernel?
For a kernel function, we have two conditions one is that it should be symmetric which is easy to understand intuitively because dot products are symmetric as well and our kernel should also follow ...
CommunityBot
- 1
1
vote
0
answers
93
views
What is the gradient of a non-linear SVM with respect to the input?
The objective function of an SVM is the following:
$$J(\mathbf{w}, b)=C \sum_{i=1}^{m} \max \left(0,1-y^{(i)}\left(\mathbf{w}^{t} \cdot \mathbf{x}^{(i)}+b\right)\right)+\frac{1}{2} \mathbf{w}^{t} \...
- 39.5k
0
votes
1
answer
953
views
Which kind of data does sigmoid kernel performance well?
While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
- 39.5k
1
vote
0
answers
66
views
Why is the accuracy of my model very low on a separate dataset from the training and test datasets?
I am working on stock price prediction project, I am using the support vector regression (SVR) model for it.
As I am splitting my data into train and test, I am getting high accuracy while predicting ...
- 39.5k
9
votes
3
answers
484
views
What is a support vector machine?
What is a support vector machine (SVM)? Is an SVM a kind of a neural network, meaning it has nodes and weights, etc.? What is it best used for?
Where I can find information about these?
- 39.5k
1
vote
0
answers
45
views
Why is my SVM not reaching good accuracy when trained to perform binary classification of search results?
I am trying to perform binary classification of search results based on the relevance to the query.
I followed this tutorial on how to make an SVM, and I got it to work with a small iris dataset. Now, ...
- 39.5k