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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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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21 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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31 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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28 views

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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6 views

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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16 views

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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13 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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9 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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37 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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27 views

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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26 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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56 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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6 views

In LibSVM, svm-scale gives data that is all 1 and -1 [migrated]

As is described in the title, when I try to use svm-scale to scale my regression data into [-1, 1], the scaled data is all 1 or -1. I've confirmed that the original data itself has no problem. I'm on ...
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31 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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20 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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33 views

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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32 views

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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25 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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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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65 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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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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67 views

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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19 views

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?
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27 views

Entire data considered as support vector

I am currently learning to use support vector machine as classification. I have a data set with 161 observation and 18 dimension. I get 160 support vectors using svm function form R package, e1071. ...
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38 views

e1071 svm queries regarding plot and tune

I am new to R and I am learning the e1071 packages' svm function. Following are the few questions I have. How does the plot function work? I cannot understand the plotting case with more than 2 ...
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optimisation procedures before training SVM

I'm using the LIBSVM in Java for classification with 200 documents in inputs. I build/train the SVM using the same input training data. My response time for preprocessing of documents (tokenization, ...
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Feature Selection using (low) MCC

I have approximately 1200 input parameters that I am trying to whittle down with the following rough process: 1) Fit rbf SVM with n = 1200 parameters and calculate Matthews Correlation ...
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Differences in SVM performance

Why would a polynomial SVM have better performance than a linear SVM but the same performance as a radial SVM?
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SVM and kernels

Suppose you are given a binary classification problem. How do you know that you have to map the problem into a higher dimensional space? In other words, how would you know that a linear SVM is not ...
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64 views

Assigning even partitions for Cross-Validation

This is a very basic question about cross-validation. Say that I have a sample size of 2901(or any difficult to divide number). How do I split this up into equal partitions (other than n=1)? And how ...
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Applying an RBF kernel first and then train using a Linear Classifier

I will start off by saying that I don't have a concrete understanding of whats under the hood of a SVM classifier. I am interested in using an SVM with the RBF kernel to train a two class ...
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142 views

High precision with low recall SVM

I'm classifying a data set using SVM and those are the precision and recall values for two classes. ...
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39 views

SVM Classification with Duplicate Training Instances

I'm using SVMs with linear kernel for sentence classification (binary). My dataset contains many duplicate instances i.e. many sentences in the training set have identical feature vectors. In the ...
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Is linear discriminant analysis (LDA) more likely to overfit than support vector machine (SVM)?

I went to a short talk and the speaker quickly mentioned something like 'LDA (linear discriminant analysis) is more likely to be overfitted than SVM (support vector machine)'. Is this true? And why?
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Logistic regression with multi-class features in R

I'm working with a data set like the following: X = ...
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79 views

How do you validate your machine learning models?

I am wondering what approaches are commonly used for validating a classification or prediction models: Approaches that am using at the moment: Using truth-sets: - ROCs, Bootstrapping, Accuracy, ...
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44 views

Kernel selection using SVM for keyword frequency classification

I have data in Weka .arff multiple-class training and testing data representing daily word frequencies in RSS feeds as follows: ...
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37 views

Interpretation C value in linear SVM

My C value is very low (close to 0). Does this mean that my feature (dimensions) have no real separative (and thus predictive) value? (As the SVM basically chooses to ignore the training data ...
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61 views

Simple SVM Question

For a linear SVM, the documentation tells me the formula is: $$ \frac{1}{2}w^Tw+C\sum\limits_{i=1}^l\xi_i$$ Please explain to me in layman's terms what w (and ξ) represent. Is w the distance to the ...
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45 views

Normalizing Vs. Scaling

Are the concepts of normalizing and scaling of data in conflict with each other? I am adding weights to my features, I have tried normalizing the weights and it didn't make any difference in the ...
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should weights be scaled too?

I am using supervised learning algorithms (specificly SVM) on my data. I know that scaling was needed for my input data. however as I am also adding weights (using pairwise comparison), I am not sure ...
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How to : a brief intro to scaling and rescaling data ( inputs) for supervised learning algorithms

I understand the concept of scaling and that it improves results in SVM's and NN's. however I would like to find somewhere where is is explained, in easy "layman's terms" terms. of how it is done. I ...