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.

Filter by
Sorted by
Tagged with
1
vote
1answer
70 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)}\...
1
vote
1answer
85 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 ...
0
votes
1answer
46 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
1answer
35 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
0answers
30 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 ...
0
votes
1answer
58 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 ...
1
vote
0answers
58 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} \...
1
vote
0answers
27 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, ...
0
votes
1answer
57 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 ...
1
vote
1answer
41 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?
3
votes
1answer
97 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 ...
2
votes
1answer
40 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 ...
0
votes
0answers
29 views

How would I go about performing a single step of gradient descent on this model?

I have a classification model that consists of a CNN followed by an SVM. I used the Keras library for the CNN portion and sklearn for the SVM portion. I am assuming I will have to fiddle with the ...
1
vote
0answers
20 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
0answers
23 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 ...
1
vote
0answers
45 views

A generalized quadratic loss and Newton iteration for Support Vector Regression, why doesn't it generalize well?

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^...
5
votes
1answer
209 views

How do I combine models trained on different data to increase classification accuracy?

I have two trained models. One is using a LinearSVC algorithm and is trained on numerical data from medical examination from patients with diabetic retinopathy. The second one is a neural network ...
2
votes
0answers
53 views

Implementation of SVM - theory to practice

I'm studying Support Vector Machines in the machine learning course, I'm a computer scientist, I've quite understood how SVM are designed thanks also to 16. Learning: Support Vector Machines - MIT. ...
1
vote
0answers
23 views

why my regression model predict every datapoint to the same value

I am trying to train a SVR but I found that with some combination of features, the trained SVR predict every point in test set to the same value. this problem occurs much more when I use linear kernel ...
1
vote
1answer
75 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 ...
10
votes
4answers
305 views

What are the domains where SVMs are still state-of-the-art?

It seems that deep neural networks and other neural network based models are dominating many current areas like computer vision, object classification, reinforcement learning, etc. Are there domains ...
2
votes
0answers
29 views

What is the benefit of scaling the hyperparameter C of an SVM?

Please read the following page of the Sklearn documentation. The figure shown there (see below) illustrates why C should be scaled when using a SVM with 'l1' penalty, whereas it shouldn't be scaled ...
0
votes
1answer
28 views

The proper AI method for modeling to predict road crashes and safety surrogate measures

I intend to use an AI method for modeling to predict road crashes and safety sorrogate measures. The dependent variables are crash frequency and other measures that are nonnegative integers. The ...
1
vote
1answer
54 views

How does an svm work? How does it perform comparisons between malignant and benign tumor

How do Support Vector Machines (SVMs) differentiate between a glass and a bottle or between a malignant and a benign tumor when it dealing with it for the first time? What will be the analysis ...
2
votes
2answers
98 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 ...
7
votes
3answers
252 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?
6
votes
5answers
500 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 ...
5
votes
1answer
574 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. ...