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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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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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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46 views

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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39 views

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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39 views

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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23 views

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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12 views

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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Pattern recognition training base division

1) A dataset (100 samples, 10 classes, 8 chars each) was multiclassed obtaining a good accuracy with an external dataset. 2) The same dataset was divided in half (50 samples, 5 classes, 8 chars each) ...
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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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18 views

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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57 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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31 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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39 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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35 views

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 ...
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Handle large set of features using SVM

I have a biological dataset with 30.000 features (genes) and 1000 data points (cells). Basically I have two major classes of cells: 1 and 0 with a distribution of 90/10. Now I am trying to classify ...
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Sales Forecasting using Support Vector Machine

I have sales data for last three years 2011-2013. I want to use Support Vector Machine technique in R to do the predictions. I just wanted to know that the approach that I am using is correct or not? ...
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libsvm with diffrent count of Keypoints [migrated]

I would like to use libsvm for a keypoint detection algorithm. Each keypoint has 36 features, but each sample of an Object has a diffrent count of keypoints... Is it even possible to train with ...
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How do you deal with different distance based features?

If I have a model where the set of features where a cosign distance measure makes sense for some of the features, and a Euclidean distance measure makes sense for the others for example using a BOW ...
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Examine SVM result by plotting histogram of decision values of training samples

I'm working for object detection(computer vision) and have some problems in SVM training. My training configuration is as below. Balanced training set (positive 3998/ negative 3998) The dimension of ...
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19 views

How should I do a grid search for the gamma in SVM?

I know that you can do a grid search for the C parameter (-c) in libSVM by going through value that go from 10^-5 to 10^5. How should I go about finding the optimal epsilon parameter (-p) ? Is ...
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Choosing fold size for highly Imbalanced dataset + nested CV + svm

I am trying to classify a dataset with ~1000 points. 90/10 is the class ratio - super imbalanced. Here are the following steps I did: Use 20 relevant features from previous knowledge Remove highly ...
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10 views

feature weights in structured support vector machine

I like to find the feature weights in a structured SVM for ranking the features w.r.t. importance. I know that in a binary SVM the weight vector can be written as a linear combination of examples. But ...
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what is the meaning of the Samples in NER?

I would like to know in NER (Named Entity Recognition ) problem , which concept should be considered as samples? each token as a sample? or each sentence ? or each Named Entity should be considered ...
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49 views

Adaptive Boosting vs. SVM

I am working on a binary classification case and comparing the performance of different classifiers.Testing the performance of adaboost algorithm (with decision ...
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Find linear SVM feature weights using libsvm

I'm trying to use linear SVM to do some feature selection. I'm using libsvm, but I cannot figure out how to find feature weights. The model file created looks something like this: ...
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28 views

How calculate average probabilities in MLP or SVM?

I have a system that find best model (best inputs and parameters of MLP/SVM) model in a financial problem for every inserted database and create a specific model for a specific data sample. I'm using ...
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63 views

Feeding a layer from a deep-learnt neural network into an SVM

In http://jmlr.org/proceedings/papers/v32/donahue14.pdf, it is stated: Our top-performing method (based on validation accuracy) trains a linear SVM on DeCAF6 Can you delineate in a way ...
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How to give an input when you are using Machine Learning method in R

I am new to R and machine learning algorithms. I have basic knowledge of different machine learning algorithms. I have four years of daily sales data.I am trying to predict sales using Support Vector ...
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38 views

Parameter optimization of SVM

Currently I am using SVM to perform some classification task. I use libSVM with Matlab interface. From the practical guide of SVM (Link), we know that there are two parameters need to be tuned, namely ...
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Polynomial Kernel

Consider the polynomial kernel: $$K(\boldsymbol{x}, \boldsymbol{x}') = (\boldsymbol{x}^{T} \boldsymbol{x}'+c)^{d}$$ What exactly is the role of $c$? If $c$ is large, does this indicate that lower ...
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35 views

Prediction using Support Vector (SV) method in R

I came to know that using SVM method we can predict the future value more accurately than other normal methods (like ARIMA). My question is how do we give the future index value (let's say 101 when we ...
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Regression vs Multiclassification

I was working with SVR, and wondering, why can't I solve a natural regression problem as a multiclassification task ? Example: I have for a regression problem: targets 1, 5 and 10, trying to fit ...
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Is there some theory of SVMs with infinitely many data?

I am trying to understand what does it means to have a (linear) SVM classifier (with soft margins) given the generative model of the data. And I realize I have not seen any paper on it, nor can I ...
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39 views

Estimate SVM a posteriori probabilities with platt's method does not always work

I have a problem.. I'm trying to create a multiclass SVM with probability output. The SVM is working so far, what means, that the accuracy is ok (see the last picture). But the probability estimation ...
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23 views

SVMs and solution

In SVMs, is the solution to the minimization problem $$\textbf{w} = \sum_{i=1}^{n} \alpha_i x_i y_i $$ and once we know $\textbf{w}$ we can get $\textbf{b}$? In plain English can somebody please ...
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26 views

Duplicate data for SVM

Can we use duplicate data as an input to SVM? The duplicate data that I mean is, let say we have 50 of same data (maybe being duplicate) from total of 100 data. Will this kind of data effect the ...
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70 views

Support Vector Machine Question

I need help with the following problem. I provided my current (partial) solution, and I hope someone can correct me and/or give me suggestions as to how I should solve the parts that I've left out. ...
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17 views

Dummy variables in logistic regression vs. svms

Suppose $y$ is a binary outcome variable and $x$ is a categorical predictor variable that takes three levels (1,2,3). In this case, you would create two dummy variables $x_2, x_3$. So $x_2=1$ if $x=2$ ...
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SVM in R package e1071

I am trying to use SVM to make a prediction (True or False) on a dataset with many independent variables. I am wondering how I can identify the most useful variable in making the prediction. I ...
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Joint label between two datasets produces significantly worse results

I have two sets of corresponding example labels with more or less the same features. Let's call first one label A and the second one label B. Both labels are binary. The classification accuracy of ...
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Can we use log-likelihood to cluster classes?

I have an SVM classifier for m classes and n data points (somewhat evenly distributed across each class). Could I use the resulting MxN log likelihood matrix to merge classes that are similar?