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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39 views

Is there any possibility of overfitting even after higher AUC, specificity and sensitivity obtained through repeated k-fold cross-validation?

I have built a model where a 10-fold cross-validation was performed 10 times. The average AUC, MCC, specificity, sensitivity of 10 times were reported as the prediction performance. Yet some people ...
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110 views

PCA shows overlapping boundaries, then why SVM performs best

I am trying to understand which model might work for a given problem before trying the models, I find this case against my knowledge. Please guide what I am missing. I am new to Data Science. Here is ...
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1answer
25 views

Finding optimal kernel parameters

I want to perform multiple kernel learning on my dataset and apply each (rbf) kernel to a different subset of features to then combine them. I do not want to have the same kernel with a range of ...
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36 views

SVM and Monte Carlo simulation to compute misclassification error rate

I am trying to solve the following problem with R: use simulation to evaluate (by Monte Carlo) the expected misclassification error rate given a particular generating model. Let yi be equally divided ...
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0answers
29 views

Identify the parameter causing the anomaly in a multivariate dataset

I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like ...
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2answers
27 views

How to solve MNP (minimum norm) problem in SVM?

I'm reading an article, which says that MNP (minimum norm problem) can be solved as SVM. In the minimum norm problem, we're given a set of points in $R^d$ and need to find a point in convex hull of ...
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1answer
26 views

Which algorithm is implemented in sklearn's SVM method?

I'd like to know which exact version of svm is implemented in slearn. The references section on sklearn's svm page cites libsvm package and a paper from 1999 which is about comparing classification ...
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16 views

Applying different kernels to parts of a dataset and merging the output [duplicate]

I am trying to create a classifier using SVM on a dataset that is composed of 6 sets of data for each of my observations. When I train the SVM (rbf kernel), I get a better performance of the ...
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18 views

How to prepare the training data for Support Vector Machine?

I'm currently doing some comparison of Naive Bayes Algorithm and Support Vector Machine classifying news to see each algorithm's accuracy. I already know how to prepare the training data for Naive ...
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1answer
36 views

Cost function in SVM

I've followed the Machine Learning course of Andrew Ng, and I really confuse in Support Vector Machine lecture. Regarding cost function in SVM, he said that when C is very large, the loss (error) ...
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13 views

Best score in SVM

i am new to machine learning and i took the house price dataset from kaggle.com to learn and understand SVM. for regression the best score would be 0.0 and for classification the best score ...
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1answer
45 views

Why do you need a balanced test set?

It seems to be the consensus that, if possible, both train and test set for binary classification should be balanced over the two classes, especially if using classifiers like SVM. Whilst I ...
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6 views

error while implementing svm using cvxopt in python

I'm trying to implement SVM from scratch using cvxopt.But I'm not getting desirable results.To debug I compared my implementation's support vectors with sklearn's support vectors using ...
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5 views

svm best paramaters

I am using python for SVM classification and I am trying to determine the optimal parameters for RBF kernel.I use grid search to determine C and gamma. I have images of 256*256 dimension (65536*33),...
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23 views

Low recall when positive is the minority class?

I have 2 versions of the same dataset, one which is fully balanced and one in which the positives:negatives is 1:2. In both cases, when I train my SVM classifier I get low recall and quite high ...
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27 views

One-Class SVM threshold parameter

I have to implement metrics FPR at 95%TPR. In order to do that I have to look for the different decisions of OCSVM dependent on the threshold. If I execute this simple code: ...
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2answers
63 views

Am I overfitting even though my model performs well on the test set?

I have a dataset with 1289 observations and around 2000 features. I split my dataset into a 70/30 training and test set. I use GridSearchCV from scikit-learn to perform 5 fold cross validation on the ...
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19 views

Assumptions of SVM

I have a dataset that has been analysed using logistic regression. In this, several variables were non-linearly associated with the log odds of my outcome, so were transformed prior to their inclusion ...
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1answer
30 views

Re-building a cross-validated SVM

Suppose we are cross-validating parameters of a Gaussian (radial) SVM on $k$ training observations. The parameters are the cost parameter $C$, and the deviation parameter $\gamma$. Then, $4k$ more ...
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3 views

Data-preprocessing for a machine learning model

I am confused about how to preprocess range based category such as age, tumor-size & inv-nodes. Should I take an average of the limits, as in - 14.5, 24.5 and so on or do one hot encoding of the ...
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19 views

SVM classification result interpretation

I have created a few SVM classifications for my data but after using polydot with a few different degrees I now have some questions. I ran one SVM using the e1071 package and got an accuracy of ~65%....
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10 views

Support Vector Machines, finding the Lagrangian multipliers and b

Hi guys, I am trying to use SVM to classify my data samples. You can find my dataset attached above where A B C are negative samples and D E are positive samples. For convenience, I have also attached ...
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14 views

Doubt about Support Vector Regression [ SVR ]

So i was studying the support vector regression, but unable to understand it fully. What i understood is, to predict the continuous value, it introduces a hyperplane with a decision boundary i.e ...
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32 views

Projected gradient descent with boxed constraints

How do you solve projected gradient descent with boxed constraints $arg min∥x−x^∗∥$ subject to $-u\leq wx_p - wx_q \leq u$
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1answer
19 views

Hyper parameter in Logistic Regression, Decision Tree and SVM?

What are the Hyper parameter in Logistic Regression, Decision Tree and SVM ?
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Meaning of support vectors in support vector regression

I know that the support vectors in a soft margin SVM classifier model essentially means the vectors on the margin or less than the margin(the ones within the tube containing the decision boundary), ...
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32 views

Sum of SVM hinge losses always a convex function?

The data loss function for a multi class SVM may take the following expression: \begin{equation} L=\frac{1}{N}\sum_{i}\sum_{j\neq y_i}\left[ \max(0,w_j^Tx_i-w_{y_i}^Tx_i+1)\right] \end{equation} ...
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49 views

VC dimension upper bound of linear kernel SVM

In the book Statistical learning theory by Vapnik, a theorem is presented regarding the maximal VC dimension of a separating hyperplane classifier (that is, in in a SVM setting). A subset of ...
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24 views

How are the convergence conditions/KKT conditions for the soft-margin SVM derived

With reference to CS229 lecture notes here, I do not understand these equations, which apparently signify the convergence conditions/KKT conditions for the SMO algorithm: I understand that the ...
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19 views

Classification Models! How to chose? [duplicate]

I am trying to transition into the Data Science field and and very curious about getting as much practical and theoretical knowledge as possible. Is there any resources that can help with identifying ...
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63 views

Hinge loss: linear or tanh activation?

My question has two parts with regards to binary classification with hinge loss and squared hinge loss. Is it correct to use hinge loss (and squared hinge loss) with both ...
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13 views

Why do linear SVMs fail on non-linear data? (mathematical explanation)

I understand the concepts behind linear and non-linear SVMs, and its very easy to picture why a non-linear polynomial kernel is required in many cases where the data is non-linear. However, I was ...
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18 views

Support Vector Classifier algorithm

I'm reading through Chapter 9 of An Introduction to Statistical Learning by James, which covers support vector classifiers. I'm unsure about some of the maths related to the algorithm that is given to ...
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35 views

SVM Regularization Term(C) = (1/lambda) vs 1/(2*lambda)

The way I learned SVM Cost Function from Andrew ng Coursera was: $$ J = \sum_{i=1}^my*Cost_1(z) + (1-y)*Cost_0(z) + \frac{\lambda}{2}*\sum_{j=1}^n\theta^2$$ which further simplifies with parameter $ ...
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1answer
48 views

Accuracy of the xgboost classifier is less than random forest?

In general the xgboost classifier is built by the idea of reducing the total error. Im using both xgboost and random forest to classify using small dataset (181 observations) and i noticed that the ...
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15 views

1D representation for 2D toy data (about linear separability)

suppose there is a dataset with 2 features x1, and x2. the points (-1;-1); (1; 1); (-3;-3); (4; 4) belong to class 1 and (-1; 1); (1;-1); (-5; 2); (4;-8) belongs to class 2. I am confused in terms of ...
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2answers
36 views

How to train model when Data is consist of matrices [closed]

I am new to ML and python. I am facing an issue related to the training SVM model. I have a training data file size (200,50,120). Where 200 are my examples (or experiments). While Actual data is a ...
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1answer
38 views

how to do the hyper parameter tunning for one class svm in r programming?

x is input (single column) tuned <- tune.svm(x=x, y =NULL, data=x, type= 'one-classification', tunecontrol = tune.control(sampling = "fix")) For this I am ...
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1answer
171 views

Kernels in SVM primal form

For a soft margin SVM in primal form, we have a cost function that is: $$J(\mathbf{w}, b) = C {\displaystyle \sum\limits_{i=1}^{m} max\left(0, 1 - y^{(i)} (\mathbf{w}^t \cdot \mathbf{x}^{(i)} + b)\...
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38 views

Multi-class classification where classes have a ranking

I am trying to come up with a Machine Learning based solution for a situation that involves automatically classifying a number of people into 4 classes - Class 1, Class 2 , Class 3 and Class 4. The ...
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36 views

How to adjust for known prognostic clinical or molecular confounders when using survival analysis methods that are not based on Cox regression?

When using penalized Cox or Coxnet regression for survival analysis, it is possible to account and adjust for known prognostic clinical or molecular confounders by including them in your model as ...
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18 views

SVM weight vector, support vectors and decision boundary understanding

I am trying to understand the relationship between the weight vector, the support vectors, and the decision boundary. Suppose that I have, the direction of the decision hyperplane [0.5 0.5 0.5 0.5], ...
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27 views

Should hyperparamater epsilon be tuned in epsilon-SVR

$ϵ$ in $ϵ$-Support Vector Regression (ϵ-SVR) denotes the $ϵ$-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual ...
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19 views

Is PCA good choice to reduce data dimensionality in this case?

I have an emotion database of 213 (with 7 classes). I used a bank of Gabor filters for the extraction of features. So I got a data matrix of 213x50000 (a huge number of parameters on only 213 images !!...
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31 views

Why don't get the expected result despite the high accuracy rate?

I have a database of images of 213 examples (7 classes). First, I extracted the features where I got 212 features. CAD, I maintain a data matrix of 213x212. I used the genetic algorithm for both ...
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1answer
30 views

Using SVM on datasets with different number of features

I'm working on building SVM classifiers on single cell sequencing data. The number of features here depends a lot on the protocol used to sequence data as well as other effects which are hard to ...
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1answer
38 views

Efficient way to evaluate an SVM classifier

I coded an SVM classifier which is giving good accuracy most of the times. To be sure of its performance, I'm generating 100 different train and test sets out of my dataset and applying my classifier ...
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10 views

Determining how many raw data items to use in machine learning training

Firstly, I'd like to mention that I'm not a statistician or a machine learning expert. I am hoping to find a starter place or advice from ML experts/statisticians here to solve a problem related to ...
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1answer
34 views

Hinge loss in SVM

I've saw the following definition of hinge loss, in the case of multiclass classification, using a delta term. $$ L({W}) = \frac{1}{N} \sum_{i=1}^{N} L_{i}({W}) + \frac{\lambda}{2} ||{{W}}||^2 $$ $$ ...
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24 views

What is the math behind predict() in e1071 for SVM? [duplicate]

I have no math or computer science training. When I run predict(svm,data,type="class") R spits out a prediction of 1 or 0 for each row of data. What is it doing ...

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