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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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 $||\beta|| = 1$ ...
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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 ...
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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.
...
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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 ...
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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?
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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?
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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.
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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. ...
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Does the image is logistic regression or SVM, and why? [closed]
Does the image is logistic regression or SVM, and why?
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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 ...
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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 ...
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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 ...
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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 ...
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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
$$J(\theta)=\sum_{i=1}^{m} y^{(i)} \operatorname{cost}_{1}\left(\theta^{T} x^{(i)}\right)+\left(1-y^{(i)}\...
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Is my 57% sports betting accuracy correct?
I have been creating sports betting algorithms for many years using Microsoft access and I am transitioning to the ML world and trying to get a grasp on determining the success of my algorithms. I ...
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Which kind of data does sigmoid kernel performance well?
While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
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How to understand mapping function of kernel?
For a kernel function, we have two conditions one is that it should be symmetric which is easy to understand intuitively because dot products are symmetric as well and our kernel should also follow ...
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Why is the accuracy of my model very low on a separate dataset from the training and test datasets?
I am working on stock price prediction project, I am using the support vector regression (SVR) model for it.
As I am splitting my data into train and test, I am getting high accuracy while predicting ...
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Is an SVM the same as a neural network without a hidden layer?
A neural network without a hidden layer is the same as just linear regression.
If I then use squared hinge loss and encoporate the l2 regularisation term, is it fair to then call this network the ...
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What is the gradient of a non-linear SVM with respect to the input?
The objective function of an SVM is the following:
$$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} \...
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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, ...
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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 ...
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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?
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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
...
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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 ...
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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 ...
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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 ...
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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^...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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