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Questions tagged [svm]

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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is permutation testing functionally equivalent to training/test?

If permutation testing is applied in machine learning, is permutation testing functionally equivalent to training/test?
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SVM training yields too many (or no) support vectors

So I implemented a support vector machine, using either a linear kernel or the rbf-kernel. I trained and tested it on a two dimensional set of data and everything seems to be working fine. However, ...
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Why setting SVDD's C-parameter to $> 1$ does not affect the result?

Why setting $C>1$ does not affect the result (compared to $C=1$) according to: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#libsvm_for_svdd_and_finding_the_smallest_sphere_containing_all_data ...
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Nested k-fold cross validation: How to choose hyperparameter for a SVM

I am currently trying to understand how exactly to use nested k-fold cross validation for hyperparameter tuning / model selection. There is one aspect I really cannot get my head around. I found ...
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Final model from nested Cross validation

I use linear SVM and have a small dataset. Because of this I decided to so nestedCV for model checking and dir obtaining the penalty Parameter C. However, I am still confused on how to get to my final ...
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How can I create a meaningful weighting for RMSE?

Background I should start off by saying I am not a mathematician and please excuse simple/stupid mistakes! The goal of my exercise is to find the “best-fitting” model for the purpose of prediction. ...
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how to do the classification problem with character features? [closed]

Im new to machine learning , I want to do the classification problem : svm , neural network , logistic , regression... for this subject "Drug Rview " in this link : http://archive.ics.uci.edu/ml/...
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Using gradient descent to train dual formulation of Kernel SVM

I've seen other posts about using gradient descent for the primal form, but not the dual form. In this book, the author discusses using (projected) gradient descent for the dual form: http://ciml....
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A question about logarithmic and wave kernel [on hold]

In most sources that I came across, I saw the dot product calculated by kernels of type listed in a topic of this question. Unfortunately, I wasn't able to find informations regarding coordinates of ...
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classification-multivariate analysis+creation of class variable

I am working on a university R project of multivariate analysis and I need some help: DATA: MIXED, with 17 variables : 4 qualitative and the 13 are continuous. PROBLEM: I don't have a class variable, ...
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Suggestions required from experts for performance improvement for a binary classification problem using timing data

I am a currently working on location verification using machine learning and neural network techniques. This is a classification problem where the system has to classify whether a user (based on his ...
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K fold cross validation sample mean question

When doing k fold cross validation, the data is randomly separated. Does it make sense to throw out a data set if it is very uncharacteristic of the general data? If I don't have a very large data ...
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If I center my kernel does it no longer remain positive semidefinite?? If so why is it being used in algorithms like kernel pca?

If I center my kernel then can it still be used in operations where a positive semi-definite kernel is required such as SVM and ridge regression? I am centering my kernel as follows: $$K_c(\mathbf{t}...
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Confusion Matrix for multiclass classification in R

I built a multiclass (11 classes) SVM model for text classification having generated a bigram from the given text. I am trying to build a confusion matrix. The output is something like this: ...
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Can One-Class SVM be used for outlier detection?

According to my readings (Support Vector Method for Novelty Detection, for instance), One-Class SVM can be used for novelty detection only. The purpose of the $\nu$ parameter is to defined the maximum ...
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SVM optimization problem: question about constraint

I am studying SVM algorithm and its optimization problem. When we are constructing optimization problem, we say, that we are searching for such separating hyperplane, so that we rescale $w$ and $b$, ...
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When is it appropriate to use Support Vector Regression?

I'm reading The Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie. In their chapter on support vector machines, they make a brief mention of support vector ...
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Example of a problem with structured output labels

I'm studying SSVM (Structured SVMs). On my book is stated that Structured SVM is an extension of the SVM, in which Each sample is assigned to a structured output label z ∈ K, e.g. partitions, ...
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Question about Validation Set for hyperparameter tuning

Okay, I'm still a bit confused as to this Training/Validation/Test Set split. I might be wrong here, but from what I understand, the model is first applied to the Training set, to "learn" from it and ...
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Odd SVM output - Need explanation

I built a linear SVM model. My data has 105 subjects and 115 features, which I ordered from least important to most important. I iterated through them to find the f1-score with all 115 features, then ...
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Programming for hybrid models of arima-ann and arima-svm time series foercasting in rstudio

i am doing my mphil thesis on hybrid modeling of arima-ann and arima-svm for time series forecasting following the G.Peter Zhang's research paper (https://www.sciencedirect.com/science/article/pii/...
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SVM and correlation

Can anyone guide me about the feature selection based on correlation using SVM? RBF kernel check the correlation too or not? I am using weka and matlab. Any help would be appreciated.
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Programming for SVM or SVR time series foercasting in rstudio

is there any programming codes for SVM for time series forecasting like neural network has build in function of nnetar in forecast package and we can also do it from caret package. if not, then how ...
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How to break down large SVM classification model?

I have a classification problem with large number of classes: feature set is 512 Dimension, number of classes are around 3000. This is a face identification problem. (identify among 3000 celebrities, ...
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Imbalanced class SVM prediction results using different validation data

I am trying to fit my data to a classifier using SVM. My data has 2 classes, the positive class which occurs with a probability of 0.002 and the negative class which is the dominant one. Suppose that ...
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Can I use svm.SVC for complex image classification?

I've been trying to implement the classical Cat vs dogs classifier using sklearn.svm.SVC. However, I wasn't able to have more than 51% correct ratio (which is ...
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Question about sample size for each class for machine learning classifiers

I'm trying to use a machine learning classifier (SVM in particular) on data that I generate. Unlike other applications, the data is not given to me but rather I have the flexibility to generate how ...
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Prove this Kernel 1 - 2* angle(x,y)/pi is positive semi definite [closed]

How should we prove this kernel is positive semi definite? K(x,y) = 1- 2*ang(x,y)/pi ang(x,y) is the angle between vector x and y
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Using SVM with only one feature

I am doing a parametric study on the performance of SVM w.r.t various input feature sets. In one case, I analyze the performance of SVM using a only one feature at a time (similar to One-at-a-time ...
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What is the effect of the use of online-learning algorithms on non streaming data?

I am wondering what the effects of using a passive-aggressive classifier instead of something like a SVM classifier on a non-streaming data would be. In other words, what are the general assumptions ...
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possible to identify (2 or more) phases (left/right step) with SVM?

I am new to the field of machine learning and I am trying to create a tool which is able to identify if a left or a right step is performed on a wearable sensor which consists out of 3 sensors (...
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how to classify input image using clustering algorithm such as k-mean?

I want to classify cifar10 images using a clustering algorithm (k-mean). Each image in the cifar10 dataset has a label, so, the results must be a set of labels which are corresponding to the test ...
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difference between RVM and SVM

What is the difference between a Support Vector Machine and a Relevance Vector Machine? What are the advantages and disadvantages of each of the type? What tasks are they good at?
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SVM model overfitting?

I have a multi-class (10 classes) classification problem. I am using one-vs-rest SVM modeling with sklearn.svm.SVC. I want to know whether my model is over-fitting. For train set accuracy is 100% ...
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Does Kernel Function Only Apply To Support Vector In SVM?

We know that the if α=0 in below equation it is not a support vector, only if α!=0, it is the support vector. L(w, b, α) = Xm i=1 αi − 1 2 Xm i,j=1 y(i)y(j)αiαj(x(i))T x(j). However, for the Kernel ...
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Fast Support Vector Machine with fewer Dimensions than Data

The paper Alex Smola and Bernhard Scholkopf, A Tutorial on Support Vector Regression states in the footnote 4 on page 2 that when the number of observations is much larger than the dimensionality of ...
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SVM classifier: strange location of support vector

I am playing around with Matlab's example which involves classifying whether data lie inside a circle of radius 1 (label: -1) or out of it (label: 1). I decided to experiment with things and flipped ...
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Is a kernel function basically just a mapping?

I'm currently studying machine learning (support vector machines to be more specific), and I was wondering how exactly I should understand what a kernel function is. I've read other questions on this ...
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1answer
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Why does Naive Bayes work better when the number of features >> sample size compared to more sophisticated ML algorithms?

According to this article Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more ...
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How can I improve my classification model(s), when it gives training, cross validation and test accuracies all close to 68%-69% only?

I'm performing a binary classification with both logistic regression and SVM, where I've 80%-20% train-test split of the 10000 samples, each with 11 features. In my problem, the features are the data ...
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what does scale hyperparameter mean in svm polynomial kernel using kernlab in r

I'm trying to train my svm model with polynomial kernel. I'm using caret package with method ...
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Classifying web pages into page type accurately

I'm trying to classify webpages as either "login", "registration/create an account", "contact us", "forgot password" for example. My approach is the following: Obtain plaintext of the page (just the ...
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Imposing Constraints on SVM Regression

I am trying to model time series data that looks similar to the made up data below ...
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1answer
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If we “delete” the term of norm in SVR problem formulation, can it be solved with simplex method?

The problem formulation in Support Vector Regression is, What if we don't want to take the "flatness" term, i.e., $\frac 1 2 ||w||^2$ and delete it; can we find solution from simplex method for ...
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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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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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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 ...