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.

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6 votes
2 answers
575 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 ...
1 vote
1 answer
252 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 ...
3 votes
1 answer
131 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 ...
0 votes
0 answers
48 views

Machine Learning Methods commonly used when data are scarse

It is well-known that deep neural networks require lots of data to perform reliably and well. A commonly-cited statistic is that you need at least 10,000 examples per class for a classification ...
5 votes
1 answer
312 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?
0 votes
1 answer
54 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?
0 votes
0 answers
31 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 ...
0 votes
0 answers
23 views

Tracing the source: Which reference did the idea of Transformer's Key-query-value come from?

Since Transformers was proposed in 2017, there have been various interpretation schemes about KQV, but the original text does not seem to explain in detail what this KQV is inspired by. I don't need ...
0 votes
0 answers
27 views

Is it possible to use a Support Vector Regression for continuous variables?

I'm working with a small dataset to predict the temperature (30 data points), as I don't have enough information, Im using RFE method so I use only the necessary features. However, I'm still searching ...
1 vote
1 answer
55 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 ...
1 vote
0 answers
51 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 ...
0 votes
0 answers
11 views

Stopping criteria for SMO when no solution exists for Hard Margin SVM

When we solve the dual problem using SMO, we pick two $\mu_i$'s at a time and optimize the dual wrt them while satisfying the required constraints. But in case we are using the no slack formulation of ...
0 votes
0 answers
76 views

In traditional R-CNN, why do we use an SVM after training a ConvNet with a log loss?

I'm learning about development of object detection algorithms and came across this fact which seems strange to me - In the multi-stage pipeline of R-CNN, after using a region extractor to crop the ...
0 votes
0 answers
19 views

Appropriate machine learning approach for object detection of connector

I am suppose to detect a female connector which happens to be an automotive part. I need to draw a bounding box around the connector when it appear. Here's the closest resemblance part I could find to ...
0 votes
1 answer
34 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 ...
0 votes
1 answer
61 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 ...
0 votes
0 answers
110 views

Is this the correct method for how to implement a stratified K fold with grid search SVC?

I'm writing a support vector classifier for a binary class using some toy data. This is the code: ...
1 vote
1 answer
144 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)}\...
0 votes
1 answer
81 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 ...
9 votes
7 answers
23k 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 ...
0 votes
0 answers
39 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
124 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 ...
1 vote
2 answers
214 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?
3 votes
1 answer
148 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 ...
4 votes
1 answer
543 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 ...
1 vote
1 answer
288 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 ...
1 vote
0 answers
12 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 ...
0 votes
1 answer
233 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 ...
2 votes
1 answer
4k 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 ...
2 votes
1 answer
114 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 ...
1 vote
1 answer
368 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 ...
13 votes
2 answers
756 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, ...
3 votes
0 answers
88 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 ...
2 votes
1 answer
195 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.
2 votes
0 answers
50 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. ...
1 vote
1 answer
61 views

Does the image is logistic regression or SVM, and why? [closed]

Does the image is logistic regression or SVM, and why?
2 votes
1 answer
92 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 ...
1 vote
1 answer
115 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 ...
2 votes
1 answer
102 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 ...
5 votes
1 answer
658 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. ...
2 votes
2 answers
1k 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 ...
1 vote
1 answer
64 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 ...
1 vote
0 answers
88 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} \...
0 votes
1 answer
637 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 ...
1 vote
0 answers
54 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 ...
9 votes
3 answers
388 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?
1 vote
0 answers
42 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, ...
1 vote
1 answer
51 views

What are the variables used in a Gaussian radial basis kernel in the context of SVMs?

If I have the Gaussian kernel $$ k(x, x') = \operatorname{exp}\left( -\| x - x' \|^2 / 2\sigma^2 \right) $$ What is $x$ and $x'$ in the context of training an SVM?
1 vote
0 answers
47 views

Scoring feature vector with Support Vector Machine

I am reading the R-CNN paper by Ross Girshick1 et al. (link) and I fail to understand how they do the inference. This is described in the section 2.2.Test-time Detection in the paper. I quote: At ...
1 vote
0 answers
42 views

Is it possible to combine multiple SVMs that were trained on sublayers of a CNN into one combined SVM?

I have created a CNN for use on the MNIST dataset for now (so I have 10 classes). I have trained SVMs on the sublayers of this trained CNN and wish to combine them into a combined SVM as to give a ...