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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libsvm: scaling data results in less features?

I've scaled my training and testing data in BASH like so: ...
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How to create a feature vector with scikit-learn? [on hold]

I have extracted some bigrams from a corpus, how can i create a feature vector with those bigrams with scikit-learn?, could anybody provide me some example?. Thanks
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How to account for different ratio of samples during training and detection using a support vector machine (svm)?

Consider the following object recognition case: Detection of objects in an image using a sliding window approach in combination with a svm model. During sliding window search using multiple scale ...
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How to find a good model for an object recognition case using a support vector machine (svm)?

Consider the following example of an object recognition case: I'm trying to detect objects in an image using histograms of oriented gradients (hog) features. The feature vector resulting from hog is ...
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Optimizing Lagrangian for SVM Alpha Values

So I have some understanding of how to optimize Lagrangian functions for single values. But for a support vector machine (SVM) we have: $$L = \sum_{i=1}^N \alpha_i - ...
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Heavy-tailed RBF kernel function

I'm trying to run a SVM regression on some data and I want to use ksvm from kernlab or svm ...
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29 views

Probability distribution of Y in regression?

I'm trying to predict the probability distribution of $Y$ given $X_0, X_1, ...$ with a nonlinear regression. The probability distribution of $Y$ is likely not normal. So far, I've set up and trained ...
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What is the relationship between vector space models & support vector machines?

Is there a relation between them? Specifically, if I have a VSM can I classify it through SVM?
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SVR overfitting

I´m using epsilon-SVR, and after a 5 cross, and testing it with an external set: ...
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LIBSVM for pre-computed kernel and zero bias (b values is zero)

I want to do binary classification and I'm using LIBSVM library for that. I have a precomputed Kernel and my bias value (b) is zero. Can I do this in LIBSVM or do I have to use some other library? ...
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Does Text Classification using SVM needs to use a dictionary to build the features vectors?

I'm kinda new to this, but I want to do an experiment I need your guys help. Open to all suggestions. Let's say I have around 5000 user accounts for which I only have several attributes [first name, ...
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Predictive model with combinations of dummy variables of different length

I would like to try to predict the amount of a public contract based on historic records where the main variables that I can fit against include: contact duration (continuous) number of buyers ...
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Usage of libsvm with RBF kernel and no Offset

I'm using libsvm for the binary classification and using a precomputed Kernel. In my particular problem there is no bias term (it's zero). Is there anyway to adjust the bias term in libsvm (and not ...
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Weka SMOreg and LIBSVM with linear kernel problems

I want to test a dataset in weka using either LIBSVM with an e-SVR or SMOreg for regression. I also choose a linear kernel in both (in SMOreg i use an exponent=1 in a non normalized polykernel). ...
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What is the toolkit that implements Cost sensitive Support Vector Machine?

I need implementation of cost sensitive support vector machine. The cost is different for each training example (unlike each class). So problem is to solve $max_\alpha$ $-1/2 \sum_{i,j} ...
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definig distances using radial basis functions

in svm the kernels are supposed to measure the distance between two vectors in the feature space. however, rbf is largest at 0 meaning that in that new space the distance between a feature and itself ...
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SVM Multiclass probability distribution

I have the following probability distribution (-b output of libsvm): ...
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Support vector regression versus kernel ridge regression

I have a question concerning the difference between support vector regression and kernel regression. I will try to write down all the math so no misunderstandings arise (hopefully). Let's begin with ...
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51 views

bad results with SVM

I have a dataset of 1157 samples. The number of features are 6 and I have only one output. My training error is very poor although I have tried a wide range of cost values and followed the recommended ...
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SQL query optimization using machine learning

This is related to my thesis work. I am trying to use SVM for query optimization. After finding the best query plan, I have to train the machine so that whenever the same type of query appears the ...
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Data normalization in k-means and svm

Generally if I want to normalize my data in which direction I should normalize (subtracting mean and dividing by std)? Lets say I have a data matrix D (...
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Why does the scaling of feature vectors improve performance of SVM classifier?

I've found that performing scaling in SVM problems really improves the performance of SVM ... But I don't understand why! I have read this explanation: "The main advantage of scaling is to avoid ...
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How we can statistically compare performance of two models before and after outlier detection?

As you know we can use Mcnemar's test to compare performance of two models in binary classification problem. But in my case i ...
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Why SVM with RBF is similar to KNN with prototypes search?

I explored similar questions and everything I can see is that both kNN and RBF are non-parametric methods to estimate the density of probability of your data. However, I am not sure if this has ...
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What is the relationship between separable SVM test error bounds and soft-margin SVM test error bounds?

Can I compute the bounds for a soft margin SVM by taking the VC dimension for an SVM and using the misclassified examples as train error? Does the inequality from wikipedia's VC dimension page hold ...
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Intuition on One Class Support Vector Machines

I am having trouble understanding how one-class SVMs work. They were introduced in a paper by Scholkopf and others (and can be found here). One-class SVMs perform "novelty detection", where a point ...
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Handling outliers in the target variable

I'm using a support vector regression model. I know the target variable has some outliers and modeling the data directly leads to bad results (Rsquare close to 0.2). I'm pretty sure the outliers are ...
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Suitabiility of the confidence score generated by SVM as a proxy for membership function

SVMs can generate a confidence score which is basically like a probability for a particular data item to belong to the particular class. I want to use this probability as a proxy for the 'distance' of ...
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Use cases for P-Kernel for SVMs

I've been reading the book by Cristianini on Kernels (2004) where generative kernels (like p-kernel and fisher-kernel, not to be confused with polynomial kernel!) are described. I am interested in ...
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predicting time series with support vector machine using R

I am planning to do time series prediction using support vector Machine. I could not find any materials about time series application of support vector machines using R or Mat-lab. Similar question ...
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Varying LIBSVM predictions based on test series labels

So I have a pretty well testing SVC train series which puts me into the mid 80 percentile without outrageous C/g values. My current C value is 2.0 and gamma is 0.5. Good numbers across the range ...
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42 views

How to prepare my data for SVM classifier in matlab

I am new to SVM and Matlab. I would like to have an example how to prepare my data to be as input to the SVM classifer (using libsvm) let us assume that i have a group of words first i have ...
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Combined Two CLassifiers

I am involved in a research where i need to classify group of words (strings) into two classes I am currently reached a dead point where my classifier is not doing as i expected. I used like three of ...
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Live selection of movie to suggest based on similarity of users

I am working with movie selection for users. 1 ) One of the first ways I thought was taking all the clicked only movie data and building decision trees out of it. Then when input is passed, the ...
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Support vector regression - data visualization

I have some training set (y1, x1) , (y2, x2), ..., (yn, xn). Output yi is a real number and the features xi live in a real d-dimensional space. I am here in the high dimension case where n = 30 and d ...
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One Class SVM strange decision boundary

I am trying to plot the decision boundary of a One Class SVM. This is a 2 dimensional representation of my training data And here the picture of the prediction obtained on the training data ...
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Linear-time approximation to kernel SVM?

Scaling kernel support vector machines to large datasets is a very challenging problem. For linear SVMs, PEGASOS is able to learn efficiently online, so training time scales linearly with the size of ...
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How do i generate variables that are relevant only for some classes?

I want to generate data for classification. I've generated data with 10 variables with two are relevant for all classes and 8 noise. now, I want to generate variables that are relevant just for some ...
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Selecting most realistic C and g params after gridsearch

I just ran an extended SVC gridsearch in libsvm on about 9000 multi-dimensional vectors representing a time series. Here are the highest scoring results: ...
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Logistic Regression, SVM or NN?

Just attended Andrew Ng’s online course on ML and although I’ve understood the methods I seem to be missing the intuition on where to apply them in terms of classification problems. What are the ...
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SVMlight support vectors inside the margin

I am trying to understand a linear model obtained with SVMlight. The model file contains a number of support vectors and their corresponding $\alpha_i y_i$. Since all $y_i \in \lbrace -1,+1 \rbrace$, ...
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How to retrieve the prediction equation in R?

I have developed a prediction model prototype in R. The model uses Support Vector Regression to predict. But I need to develop the entire solution in Visual C++ for a real life implementation. I ...
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How to choose negative training sample for Classification problem

Choosing positives sample is a relative straightforward task, but I'm having some problem on determine what should I use for the negative example. I'm working on a SVM binary classificator, trying to ...
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Linear SVM prediction time is scaling in an unexpected manner based on training data

I'm using LIBSVM to do some training as it was recommended by Andrew Ng and is used under the hood in SciKit Learn. LIBSVM is doing something different than what I expect though: My beliefs are as ...
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Most efficient vector construction for Dynamic Time Warping

I'm in the process of folding FastDTW into my SVM and the question now is how to best format my data (irrespective of normalization). Here's an example of what I'm attempting to do - given two 3d ...
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Feature boosting via rescaling in logistic regression and linear SVMs

If I were expressing a problem in terms of binary features, all encoded as {0,1}, could I boost some features by encoding them as {0,2}? Would the effect change based on whether I used either of the ...
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108 views

How to choose the training, cross-validation, and test set sizes for small sample-size data?

Assume I have a small sample size, e.g. N=100, and two classes. How should I choose the training, cross-validation, and test set sizes for machine learning? I would intuitively pick Training set ...
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43 views

Instance weighing in libsvm/liblinear

I often use the instance weights with Libsvm for classification problems. http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances Does anyone know the details of the algorithm that ...
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53 views

How to format multi-row time series data for LIBSVM regression

I would have expected this to be covered in detail by the LIBSVM tutorial but after hours of wasting time googling for answers I've had to throw in the towel. What I am trying to do is rather trivial ...
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SVM versus Bayesian regression example(s)?

I am trying to track down examples where some basic problems have been tackled via both classical Machine Learning algorithms and more formal statistical methods. In particular, I'm interested in ...