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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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SVM Kernal the correct approach for classification problem?

I am working on a classification model that I will explain below and I wanted to get some insight on the optimal approach. I am currently experimenting with SKLearn and SVM Kernel as such svm.SVC(...
Ahmed Zaidan's user avatar
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Linear SVM Hyperparameter Selection

I'm trying to train a linear SVM on the CIFAR-10 dataset and I obtained the results in the plot below for the hyper-parameter tuning (learning rate and regularization strength). It looks like the ...
timu vlad's user avatar
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Support Vector Machine used in Image Classification

I am looking for a reference preferably a paper that details how Support Vector Machines (SVMs), and OpenCV were used to perform image classification before Convultional Neural Networks (CNNs). I am ...
Jose M Serra's user avatar
1 vote
1 answer
57 views

Suitability of Gaussian RBF (radial basis function) in SVM to separate the two classes

Given the following data samples (square and triangle mean two classes), why is it suitable to use a Gaussian RBF (radial basis function) in SVM to separate the two classes?
DSPinfinity's user avatar
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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 ...
Riccardo Caiulo's user avatar
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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 ...
DSPinfinity's user avatar
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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 ...
Papyrus's user avatar
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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 ...
lonewolf.py's user avatar
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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 ...
whatamnotsaying's user avatar
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1 answer
207 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 ...
Mayur Kulkarni's user avatar
5 votes
1 answer
396 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?
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1 answer
157 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?
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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 ...
root's user avatar
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1 vote
1 answer
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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 ...
Foobar's user avatar
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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 ...
Allie's user avatar
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1 answer
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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 ...
alexand88r's user avatar
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1 answer
67 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 ...
Philipp Kunz's user avatar
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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 ...
Taqi Ahmed's user avatar
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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 ...
Dae-Young Park's user avatar
1 vote
0 answers
26 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 ...
user8714896's user avatar
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 ...
hanugm's user avatar
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1 answer
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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 ...
Hrushi's user avatar
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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 ...
user871621's user avatar
1 vote
2 answers
486 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?
Emiliiii's user avatar
2 votes
1 answer
242 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.
axelmukwena's user avatar
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. ...
The Pointer's user avatar
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?
user5520049's user avatar
7 votes
2 answers
895 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 ...
The Pointer's user avatar
2 votes
1 answer
130 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 ...
The Pointer's user avatar
2 votes
1 answer
184 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 ...
Joneron's user avatar
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1 answer
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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 ...
alighorbani's user avatar
1 vote
1 answer
175 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)}\...
jr123456jr987654321's user avatar
1 vote
1 answer
481 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 ...
Sports_Stats's user avatar
0 votes
1 answer
1k 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 ...
Gooday2die's user avatar
1 vote
1 answer
85 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 ...
christopher's user avatar
1 vote
0 answers
72 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 ...
Debugger's user avatar
1 vote
1 answer
375 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 ...
FeedMeInformation's user avatar
1 vote
0 answers
99 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} \...
FeedMeInformation's user avatar
1 vote
0 answers
46 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, ...
iamPres's user avatar
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1 vote
1 answer
293 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 ...
FeedMeInformation's user avatar
1 vote
1 answer
74 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?
FeedMeInformation's user avatar
3 votes
1 answer
161 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 ...
boomselector's user avatar
2 votes
1 answer
158 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 ...
Jimmy116's user avatar
1 vote
0 answers
58 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 ...
JVGD's user avatar
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1 vote
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79 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 ...
FeedMeInformation's user avatar
1 vote
0 answers
49 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^...
filippo portera's user avatar
5 votes
1 answer
1k 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 ...
Aleksander Chmielewski's user avatar
4 votes
1 answer
1k 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 ...
Marco's user avatar
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1 vote
0 answers
29 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 ...
rashford10's user avatar
3 votes
1 answer
178 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 ...
nbro's user avatar
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