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Support Vector Machine refers to "a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis."

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Why does changing random seeds alter results?

I'm running some SVMs for a seminar and a friend of mine noted I should set a seed so my results don't change everytime I run the code. I was wondering why is that the case. If a different seed can ...
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Using TensorFlow sentence encoder and other parameters as features in SVM

I have 150K tagged samples of technical support chats between customers and technicians. The chats are classified into 2: “resolved”/ “unresolved” sessions (66.6% and 33.3% of the distribution ...
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What machine model can balance bias and variance trade-off at testing time?

I wonder if there is a machine learning method can be trained to converge, and then balance the bias-variance trade-off during testing time with some hyperparameters? For example, K nearest neighbor ...
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Type error trying to use svm module in sklean [closed]

I'm trying to implement an SVM in python 3, using sklearn. This example ...
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Where does “ρ” come from in the primary objective function ?

I'm trying to fully understand the primary objective of one-class SVM function. This function is defined in paragraph 3.2 of Enhancing One-class Support Vector Machines for Unsupervised Anomaly ...
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Review 5-stars Multiclassification Model

So I'm super new to DataScience World. And I'm Trying to do a TextMining Work. My goal is by reading user's reviews to predict their rating to a tech product. Problem? Multiclassification model with ...
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1answer
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p < 0 when using SVM for regression with R [closed]

I am working on a regression problem, trying to predict the age of subjects from a set of biomarkers. I've employed various methods of both feature selection/dimensional reduction (RFE, PCA, PCA/ICA ...
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19 views

How to choose hyper-parameter for Gaussian Process kernels?

I'm trying to fit Gaussian Process in scikit-learn, and start with using kernel = RBF_1 + RBF_2 + whitekernel(sum of two RBF kernels with different length_scale and ...
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1answer
42 views

Is an geometric approach of support vector machine part of supervised learning?

The support vector machine can be approximated geometrically by enclosing the data of the classes by convex hulls. Then you can e.g. using the Rotating Calipers, you can search the widest band/border ...
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Three possible models for time series classification with many correlated variables

I have a dozen datasets and they all have the same structure: the dependent variable is a column of $0$'s and $1$'s. The number of observations labeled as $0$ is roughly 20 or 30 times the number of ...
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Why do we need the gamma parameter in the polynomial kernel of SVMs?

The polynomial kernel is sometimes defined as just: $$ K(x,y):=(\left<x,y\right>+c)^d $$ with two parameters: the degree $d$ and constant coefficient $c$. But others (e.g., libsvm, and sklearn ...
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Is maximizing margin the same as minimizing the number of misclassifications?

I'm relatively new to ML and I was just studying SVM for classification. From my understanding of SVM, we wish to "find the hyperplane which maximizes the margin between classes." In theory, is ...
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19 views

Is there any paper shown how to solve an one class SVM by SMO type algorithm

The one class SVM can be used as an outlier rejection. An example can be found on. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041295/ Generally one class SVM is shown as a constrained quadratic ...
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Precomputed Kernels for Support Vector Machines (SVM)

To calculate the linear kernel matrix for some training matrix X with dimensions n x d where d is the number of features and n is the number of data points, we can simply do: $X * X^T$. The result is ...
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44 views

How to train SVM with HOG feature?

I'm using the MultiClassSupportVectorMachine class to do classification for face detection. Here is code sample from accord.net documentation with changes in parameters: ...
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34 views

Support Vector Machine Kernel Choice and hyper-parameter tuning for high class imbalance data

Thanks for your help in advance. My question is this: Given the below information, is there some kernel preference and particular hyperparameter that is preferable to use when dealing with high class ...
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25 views

Unable to solve using lagrangian multipliers

Suppose $$K(x,z) = \theta(x)^T \theta(z) = \left\{ \begin{array}{ll} 1 & \text{if } x = z \\ 0 & \text{otherwise} \end{array} \right. $$ and $y_1=+1$ or $-1$. I ...
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The predictive error with the degree of the polynomial kernel?

For kernel SVM, I figured that predictive error getting bigger with the degree of the polynomial kernel goes higher. Does anyone know why is that?
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59 views

How are support vectors in SVM equidistant?

In most literature on Support Vector Machines,we start with a decision boundary $\vec w\cdot \vec x+b=0$. The support vectors are then assumed to lie on the planes $\vec w\cdot \vec x+b=1$ and $\vec w\...
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Multiple kernel learning on gram matrices

l'm looking for a Multiple kernel learning algorithm such as simple MKL that do the following: Given 6 features matrices ...
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24 views

Calculating the optimization problem for a SVM in python

I am trying to understand, how SVMs work internally. Let's say, I have these two vectors $\begin{pmatrix} 1\\ 3 \end{pmatrix}$ and $\begin{pmatrix} 1\\ 1 \end{pmatrix}$ and the labels are 1 and -1....
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Confusion on hinge loss and SVM

I'm reading a book on data science and get confused about how the book describes the hinge loss of SVM. Here is a figure from the book on Page 94: This figure shows the loss function of a NEGATIVE ...
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1answer
26 views

Understanding the equations for support vectors in SVMs

I am struggling with this point (since my Math is abysmal). I have looked at various YouTube videos (most of them just gloss over this or the Math is beyond me) and read various blog posts (same ...
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1answer
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Basics for the positive support vectors in the support vector machine

Can anyone explain in detail why $wx^++b=1$ for positive support vectors in SVM and $wx^-+b=-1$ for negative support vectors in SVM?
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Question about Geometric Margin of Support Vector Machine

I'm trying to follow Andrew Ng's notes on Support Vector Machines and had the following question. In his notes, Ng, transforms the following optimization problem [using the notion of geometric ...
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14 views

SVM Regression n R

Hi The following graph represents original data, linear regression and Support vector regression. I would like to know if this is a decent plot of SVR and not understanding how to predict for a new ...
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Support Vector Machines VS LSTMs: How well it is justifiable to use LSTM for its Generalization properties?

The question is pretty straightforward, How well one can justify using LSTMs(Neural Networks) for text classification task in terms of "Generalization" compared to classic support vector machines(SVM) ...
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SVM optimization problem with constraint

I am studying SVM from Andrew ng machine learning notes. I don't fully understand the optimization problem for svm that is stated in the notes. So we have optimization problem $$\max_{\gamma, w, b}\...
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Judge the performance of SVR in R

I am analyzing a dataset by using support vector regression in R and try hard to figure out the answers to the following points... but in vain so far. Any help and guidance will be deeply appreciated. ...
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Use the features selected with RFE SVM linear for prediction of SVM rbf

I was wondering if the features selected with RFE with SVM linear kernel are still "good" features when we use a non linear model, like SVM rbf kernel. This comes in practice when you want to use SVM ...
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14 views

How to plot support vectors for SVR model's prediction

I've got a code for SVR on simple dataset with single parameter in X (position level) and salary as y output. I obtained a plot of prediction: Is it possible to plot the support vectors as well? I ...
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Penalties In Soft margin SVM

In Soft Margin SVM, The Penalty $\xi_i$ , is given by $\xi_i = 1- y_i(\omega x_i+b)$ , if $x_i$ is on the wrong side of the margin i.e ($x_i $ is incorrectly Classified) From where we got this $\...
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1answer
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SVM - Kernel Expansion

I have the following statement in a text: I am not sure how the following expression was expanded to such. Where did the square root 2 come from, and how come we have an i and j iterator in the first ...
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How are functional margin and geometric margin used in SVM?

I believe, geometric margin is euclidean distance between the point and hyperplane, whereas the functional margin just gives the confidence. At which stage is geometric margin and functional margin ...
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Why are the support vectors in an SVM classifier on hyperplanes that are 1 or -1 away from the wanted hyperplane

The way it is described here https://www.svm-tutorial.com/2015/06/svm-understanding-math-part-3/ , the wanted hyperplane is found by finding two other hyperplanes which have the equations $\vec{w}\...
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Machine learning parameter tuning using partitioned benchmark dataset

I know this will be very basic, however I'm really confused and I would like to understand parameter tuning better. I'm working on a benchmark dataset that is already partitioned to three splits ...
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How to perform kFold cross validation in Libsvm's precomputed kernel in MATLAB?

I understand that Libsvm provides 'v 10' option for 10-fold cross-validation in...
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Is a SVM (+Boost) faster than a NN to train with similar accuracy?

I might possibly misunderstand something here. So please do tell me if I do. At the moment, I am doing some research regarding the use of machine learning to detect a certain object. Currently I am ...
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How to combine/aggregate classification accuracies from binary one-vs-one classifiers to get final accuracy equivalent a multiclass classifier?

Consider a 3 class data, say, Iris data. Suppose we want do binary SVM classification for this multiclass data using Python's sklearn. So we have the following ...
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How could a variable length binary string be encoded as an SVM feature?

I have data which is a binary string, e.g. 10001001 or 111100000001. The length can vary between 3 and 13 characters in length. It represents a pattern found in nature where the length is variable ...
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Suitable Machine Learning Classifier for Numerical and categorical dataset?

Does anybody know! what are the suitable machine learning algorithms --e.g., bayesian network, decision tree, OneR, etc.-- to learn the model from a dataset with limited instances --e.g, less than 10 ...
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Plot ROC or PR curves from either the X,Y coordinates (i.e TPR/TNR; or PPV/TPR) or list of predictions (class probabilities)? [closed]

I have a list of X,Y coordinates for plotting both a ROC curve and a PR curve. I also have the data which was used to calculate those coordinates (i.e. a list of individual predictions with binary ...
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Splitting strategy for a multiclass problem

I have a 16 class data , Indian Pines , of dimensions 145x145x200. From the data size , I am unable to gauge whether to do stratified splitting , random splitting , or plain train-test split , before ...
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25 views

regression accuracy in matlab

I have dataset that kind of climate features (11 attributes) hourly and electricity consumption in non-residental building, I test Regression (Tree, svm (guassian), Random forest and deep learning), ...
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1answer
101 views

SVM classifier convergence

I was implementing a SVM Classifier using scikit library on a MNIST dataset available on Kaggle. Everything was going well until one of my friend asked me a ...
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Nested cross validation with balanced down-sampled training data set - fairness and proper method?

Example data set: 150 positive examples (fold size = 15; 9 folds = 135). 1770 negative examples (fold size = 177; 9 folds = 1593). The problem: When performing ...
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Why do CNNs conclude with FC layers?

From my understanding, CNNs consist of two parts. The first part (conv/pool layers) which does the feature extraction and the second part (fc layers) which does the classification from the features. ...
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How to pre-process features from different domains for Machine Learning models [duplicate]

Eventhough my question is applicable for all kind of models, I asked it in the scope of SVM for now. Assume I have 3 sentences in my training set and 2 sentence in my Test set. I would like to ...