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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Measuring Accuracy of the SVM based model

I have developed a model which evaluates a user based on how important he is for the organization. For that purpose I have generated 1000 records for 1000 users. Here I have one dependent variable "...
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9 views

Obtaining exact output values of SMO based classification, before clearly demarcating them into classes

While running the SMO classifier in weka, if I have inputted my training labels as 0 and 5, (A binary set), then while running the classifier model on test data, are the outputs some decimal values ...
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1answer
18 views

What will be the input value (i.e. $x$ and $x′$) of RBF kernel for a given dataset or data matrix $x$?

If $x$ is a data matrix or dataset then What will be the input value (i.e. $x$ and $x'$) of RBF kernel $K_r(x,x')=\exp(-\frac{\|x-x'\|^2}{r})$ ? I can understand $x$ is same as dataset or data matrix ...
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1answer
20 views

R e1071 SVM always gives me (in average) below changes cross validation accuracy with random data

I am running e1071 linear SVM on my neuroimaging data. ( by function svm() ) When I was doing permutation tests, I found, in average, the cross validation (CV) accuracies with shuffle labels were ...
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8 views

How to use Artificial Bee Colony to optimize the gamma and sigma^2 parameter of LSSVM? [on hold]

I have downloaded an Artificial Bee Colony package from http://yarpiz.com/297/ypea114-artificial-bee-colony and tried to apply it into my work on finding ways optimize the gamma and sigma^2 parameter ...
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8 views

How to calculate multi-class prediction probabilities using One vs All and One vs One classifiers

To be specific, let's consider Support Vector Machines. In the 1 vs all case, one has $n$ binary classifiers where $n$ is the number of classes. In each classifier one has two labels 0, 1 and for a ...
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5 views

how to define the Features,labels and function in sequentialfs matlab [closed]

I have the features extracted using LBP for 5 classes of size 645 X 40960 and labels of size 1X645. Now i want to select the relevant feature using sequentialfs..by going through matlab help..i tried ...
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14 views

Can binary svm have labels 1 and 26(any number but not 0,-1 or 2)? [duplicate]

I trained binary svm with lables 1 and 26. but while classification it returned a label of 1 and 2 which i have not mentioned during training. i thought it would return 1 or 26.
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43 views

Big difference of accuracy between C-SVC and nu-SVC using SVM

I'm currently dealing with an image classification problems. The objective is to classify the images to 4 classes, with 8000 images in the training set and 14000 images to predict. I'm using the SVM ...
3
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1answer
43 views

Normalise X/Y coordinates to stop jitter

Disclaimer: I am a programmer by trade, not a statistician, so please cater to my ignorance and I apologize now if I make any incorrect assumptions Please consider the following problem: I am using ...
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1answer
35 views

SVM training and testing error interpretation

I am new to machine learning and I just used SVM for the first time to analyze my dataset... Now I have created a figure that displays the training and testing error of the model as a function of ...
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15 views

The same input and different output form one-class SVM

I have applied scikit-learn OneClass SVM classifier to isolate the noisy tweets as outliers using one class training set. We use TfidfVectorizer class from sklearn to convert a collection of raw ...
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14 views

Reference request about feature maps in ML

Can someone kindly link to some recent papers on understanding feature maps in ML? It would help to get an idea of what are the recent issues there that people have been working on with regards to ...
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0answers
12 views

Building a hierarchical classifier

I'm trying to build a hierarchical classifier on R. I know my data has a tree structure with a root node + 3 levels and that each node on a level has 10 nodes coming out of it. ...
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1answer
24 views

How to justify the usage of “Radial Basis Function” as a kernel for SVM [duplicate]

I ran SVM with several kernels on my data. RBS has the best performing results. The task is similar to the text classification. I wonder how I can explain why RBS is actually the best kernel for my ...
3
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1answer
40 views

Why must kernel functions be scalar products [duplicate]

I'm currently reading Bishop's Pattern Recognition and Machine Learning. In the chapter on kernel methods, he's very clear that kernels must be "valid", that is: be representable as scalar products in ...
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2answers
30 views

How do you know that the assumptions of the model have been satisfied, and it’s ok to run the algorithm? [closed]

when using any simple algorithm like logistic regression, svm or even complex ones, how could you know that it’s ok to run the algorithm and use it in the industry? for example :when using logistic ...
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1answer
79 views

TOO low estimated SVM probability for most of the negative test examples?

I am using LIBSVM (as well as the fitcsvm and fitSVMPosterior of ...
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16 views

Labeling methylated/unmethylated sequences [closed]

I'm trying to do some test with matlab/SVM to classify methylated/unmethylated human sequences. I'm using these sequences freely avaible at this address: http://services.ibc.uni-stuttgart.de/name21/...
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23 views

What is the purpose of Primal and Dual problem in SVM? [duplicate]

I am trying to understand the Primal and Dual problem in SVM. As far as I understand - they both are way how to find the maximal margin. But the Dual supposedly using Kernel function somehow, so it is ...
2
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0answers
36 views

Help interpreting formula for multi-class hinge loss

As I'm reading from wikipedia, and this Cross Validated question: Gradient for hinge loss multiclass, the gradient value for a training feature set is somewhat straightforward. However if I'm ...
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18 views

Is there a one-class hinge loss function?

Is there a [hinge] loss function that is suitable for one-class classifiers? i.e., anomaly detection. Thanks.
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1answer
41 views

How does gamma in SVM RBF kernel influence the accuracy?

I am working on a classification program using SVM RBF kernel. To find the best parameters C and gamma, I used grid search, and got the image below. What confuses me is that when gamma varies from 0.3 ...
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1answer
57 views

Using Random Forest Variable Importance to train SVM models (R)

I have trained a Random Forest model in R with the caret package but the results are not very promising. I have decided to try with SVM models but I have a great ...
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1answer
49 views

What is the right way to use SVM with cross validation?

I read a lots of discussions and articles and I am a bit confused on how to use SVM in the right way with cross-validation. If we consider 50 samples and 10 features describing them. First I split ...
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22 views

Learning with unbalanced classes

I have a biometric data set of size $n \sim 150,000$, with $p=21$ features $x_1,\dots,x_p$ representing information about people $-$ some categorical and some numerical. The original data set had only ...
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20 views

Unequal misclassification costs for SVC?

I wonder if there is a way to specify custom cost function in sklearn/python? (atm I use sklearn SVC) My real problem has 7 different classes, but to make it more clear lets assume that I want to ...
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14 views

How can I use gradient descent on the dual form of the linear SVM problem?

I understand that this is the dual form of the linear SVM problem (with a hard margin): $J(\mathbf{\alpha}) = \dfrac{1}{2}\sum\limits_{i=1}^{m}{ \sum\limits_{j=1}^{m}{ \alpha_i \alpha_j y_i y_j {\...
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9 views

How is the linear SVM's cost function derived from the Lagrangian (for the primal form)?

This is the cost function that seems to be used for training linear SVM classifiers: $J(\mathbf{w}, b) = \dfrac{1}{2} \mathbf{w}^T \cdot \mathbf{w} \quad + \quad C {\displaystyle \sum\limits_{i=1}^{m}...
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1answer
31 views

How does alpha relate to C in Scikit-Learn's SGDClassifier?

I'm trying to get the same linear SVM classifier model by using Scikit-Learn's SVC, LinearSVC and ...
3
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1answer
17 views

What's in a name: Kernel [duplicate]

I wanted to know, for my intuition, why the name 'kernel' is used for kernel density estimation. I am also curious for the 'kernels' in support vector machine if they are referring to the same name. ...
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8 views

Getting probability estimates for each observation from cross-fold validation in SKlearn?

Let's say I have a model that I want to retrieve cross-fold validated probability predictions from. I can divide my data set into n folds, leave one fold out, get probability predictions for each ...
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2answers
27 views

How to check if the optimization algorithm produces the correct result [SVM]

I have implemented the gradient decent algorithm for the SVM (linear case). I would like to check if my implementation is correct. I see 3 possible solutions: Write some unit tests for the code ...
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30 views

SVM subgradient-descent (Pegasos algorithm)

I am trying to implement the Pegasos algorithm for large scale SVM training. I'm following the main paper Pegasos. Everything worked fine but the results are quite disappointing. The code: ...
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6 views

Main assumptions of longitudinal support vector classifier

When looking for methods to perform classification on longitudinal data, I stumbled upon Longitudinal support vector classifiers, which seeks to find not only the hyperplane parameters $\alpha$ but ...
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30 views

Changing SVM tune performance measurement method (e1071 tune function)

Hi I am using e1071 package for SVM. I am using the tune() function to find the best parameters. It seems to me the default prediction function used is RMSE. However, I am having a binary ...
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2answers
54 views

Why does SVM needs to keep support vectors?

I am reading the book Artificial Intelligence a Modern Approach and I have trouble understanding why the SVM needs to keep support vectors. From the book: SVMs are a nonparametric method -- they ...
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29 views

Counter intuitive behavior from scikit-learn's SGDClassifier

I am working with SGDClassifier from Python library scikit-learn, a function which implements linear classification with a ...
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2answers
71 views

Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?

Why do people use Quadratic Programming techniques (such as SMO) when dealing with kernelized SVMs? What is wrong with Gradient Descent? Is it impossible to use with kernels or is it just too slow (...
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3answers
143 views

Raw data outperforms Z-score transformed data in SVM classification

I've been trying to perform a binary classification using an SVM classifier (scikit-learn's SVC with RBF kernel). I have a sample size of about 100, with about 70 features each. The features are of ...
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12 views

picking a kernel SVM, how to normalize categorical + numerical data

So I have a dataset that contains both categorical and numerical data for each data point, and a class for each data point. My goal is to plan to build an SVM model from the data to predict the class ...
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9 views

Why am I getting the same value for F1 and accuracy?

I trained and SVM classifier and I noticed that I'm getting equal F1 and accuracy values (using a cross-validation), which means that the number of True-Positives and True-Negatives is the same. The ...
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2answers
269 views

Can a linear SVM only have 2 classes?

Can a linear SVM support more than 2 classes for classification?
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2answers
54 views

Scikit-learn's SGDClassifier code question

I have a question regarding the code of function SGDClassifier, from library scikit-learn, which implements linear ...
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29 views

Difference between naive Bayes / multinomial / Bernoulli / SVM

i have a question to what consider the Naive Bayes algorithms, i am confused about the difference between the 3 algorithms: 1)The original Naive Bayes, 2)Bernoulli Naïve Bayes, 3)Multinomial Naive ...
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8 views

How to select weights in the F-Measure to be aligned with that used in cost-sensitive SVM training?

I am dealing with a classification problem in which Recall is more important than Precision, and the training dataset is an imbalanced one. The approach I am taking is to use oversampling to mitigate ...
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25 views

What is a hard data set for support vector machines?

Suppose that: There are only numeric features in the data set. There is a binary target variable (since a general nominal case is resolved by 1vs1 or 1vsTheOthers approaches). I have noticed that ...
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7 views

Circularity of Gaussian kernels and infinite dimensionality [closed]

I'd like to know more about this statement: Gaussian kernels are circular (which leads to the above-mentioned infinite dimensionality?) Questions: in image processing the issue of ...
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1answer
30 views

how to classify short text sentences?

I have a very large dataset that looks like string x this-is-a-nice-sentence 1 hello-my-name-bird 0 yay-this-is-awesome 1 ...
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1answer
34 views

Why is SVM calculated in this way?

I have a question regarding SVM. I understand the Lagrange equation, $L(w,b,\alpha) = \frac{1}{2}w'w - \sum_i \alpha_i (y_i(w'x_i+b)-1)=$ $\frac{1}{2}w'w - \sum_i \alpha_i y_i(w'x_i+b)+ \...