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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Why the Brier Score's better when probabilities are estimated through PAVA instead of Platt Scaling?

I've been studying (and applying) SVMs for some time now, mostly through kernlab in R. ...
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Does it ever make sense to use a feature-local (e.g. polynomial) kernel for binary data?

As I understand, for a sample $s$, a polynomial kernel produces a vector consisting of $x_{s,i},x_{s,i}^2,..., x_{s,i}^n$ for every feature $i$, allowing SVM (or ANN) to effectively find a nonlinear ...
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Can I run an SVM on sparse temporal data without a regular time interval?

I have data of occurrences with timestamps that could be days or months apart. I'd like to enter the values natively as follows. Are there any SVM algorithms that can support such an input? day 1: ...
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Svm for survival analysis in r [on hold]

Is there any implemented package for survival analysis with SVM in R? I need to feed the model with both survival time and event. Thanks!
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58 views

how to solve an optimization problem? [on hold]

I am reading an article with title "Learning a distance metric from relative comparisions" lately. http://www.cs.cornell.edu/people/tj/publications/schultz_joachims_03a.pdf. The basic idea for this ...
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Handling categorical predictors in logistic regression, linear regression and SVM

I want to know how I can handle categorical variables in logistic regression, linear regression and SVM. The categorical variable has four categories 1,2,3 and 4. However, it doesn't mean 4 is like 4 ...
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73 views

Why is SVM better for bioinformatics analysis?

I have used five different algorithms: bagging, boosting, C4.5, random forests and SVM, for binary classification of biological data relating to peptide sequence. The dataset comprised of ...
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27 views

How to Filter Junk Features Automatically

A data set that is used to build a regression model might contain "junk" fields. For example if I want to build a model of house prices, the field number of rooms and the size of the house are ...
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44 views

train an SVM via back propagation?

I was wondering if it was possible to train an SVM (say a linear one, to make things easy) using back propagation? Currently, I'm at a road block, because I can only think about writing the ...
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SVM for regression : confidence intervalls

I use the svm function (for regression) to make forecast like I would with for exemple the arima function: fit<-auto.arima(ts) ...
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R | NA/NaN/Inf in foreign function call | e1071 SVM [migrated]

Dataset: https://archive.ics.uci.edu/ml/datasets/Chess+%28King-Rook+vs.+King-Pawn%29 Code: ...
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What is “Multi-Scale” Grid Search in SVR Cross Validation

I am trying to implement an algo that uses epsilon-SVR with a histogram intersection kernel. The slack variable C and the error intensive margin e needs to be optimised. The algo uses an exhaustive ...
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Help me with different formulations for SVM classification

I've been working with kernlab for more than a year now, but I always stickied to the vanilla cost (C-svc) formulation for ...
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Nested Cross validation vs Ordinary CV

Usually nested cross validation procedure is used when the tuning parameters of the model are estimated simultaneously to the model assessment. According to the theory, the ordinary CV is not suitable ...
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A small help in understanding basic SVM

I am on the course of learning SVM. So, I am having a doubt. Suppose in the case of 2D, a point needs to be classified. So, let say I am having a point x(2,3). So according to the equation wx+b >= 1 ...
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Linear SVM - huge differences between libraries' accuracy

I use two libraries to perform SVM: liblinear and sofia (implements PEGASOS). I train them both on the same data (linear SVM), and then test them on two different data sets. The accuracy is very ...
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23 views

Polynomial curve fitting and support vector machines

I applied regression using support vector machines and then I approximate the results using polynomial regression and obtain an equation for the results. I applied support vector machines and then ...
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How can I access to a element of a svm.pred object?

I'm trying to predict the class of a sample (predict the quality of a wine, in levels of 4-5-6-7-8) with a SVM multiclass. When i predict a single sample in this way: svm.pred <- ...
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Nested Cross Validation

I am training a linear classifier using SVM. I used nested cross validation. Inner loop is used to estimate the best C (cost parameter) and out loop uses the best C to train and test the classifier. ...
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25 views

Building an image classifier( binary) using Convolution network network

I have a dataset of images of birds and want to build a CNN classifier that outputs the probability that the fed image(test) is a bird, So that I can accept the image to be a bird beyond a certain ...
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Adding hard conditions/rules during machine learning

I am training a LinearSVM for a text classification task. In my data I have some instances that can be classified as 1 but I want to set some hard conditions or rules during the learning stage and ...
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21 views

How to preprocess a large sparse matrix and unbalanced classes in machine learning

I have a large very sparse matrix with 1000 columns and 15000 rows. It mainly contains zeros, the rest is integer values from 1-8. I'm limited to scikit-learn and ...
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30 views

Resources for machine learning for time-dependent data

For the past year, I have spent the majority of my free time learning a variety of ML techniques (boosting, random forests, neural nets, SVMs etc.), but I have not been able to find a lot of material ...
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Principal Component Analysis and Support Vector Machines

I have a data set with roughly 65 000 records, 100 predictors, a single continuous response that was converted into a binary response. I used pca with logistic regression and got good results. I would ...
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32 views

How to use Particle Swarm Optimization for finding hyper-parameters of Support Vector Regression?

I want to use Particle Swarm Optimization (PSO)for finding hyper parameters of a support vector regression problem. Initially I tried to find the same using grid search method,but the Matlab code is ...
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36 views

Chi Squared Kernel and Faster implementation

There is a good implementation of Chi-Squared Kernel in http://www.vlfeat.org/matlab/vl_alldist2.html But this implementation is very slow when input data is huge. This implementation doesn't accept ...
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Classification in SVM

I am not expert in this area so please bare with me. As far as I know the SVM is used for distinguishing or classifying two sets. My questions are: Is it possible to classify more than two sets or ...
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Understanding Platt Scaling: target probabilities

Ref. to his paper: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=8449CED7A9FFE1AB5009ED3AEAAA545A?doi=10.1.1.41.1639&rep=rep1&type=pdf On page 6, he states "..sigmoid can still be ...
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How to prove that text is linearly separable?

I sentiment analisys task, for this I used SVM with an rbf kernel and a linear one. The results for the linear kernel were better than the rbf, from this I know that text is linearly separable, but ...
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Bias terms in SVMs

I'm training multi-class classification models using linear SVMs - by learning binary one-vs-all classifiers for each class. To classify a test instance, I evaluate the following equation for each ...
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svm functional margin and geometric margin

I know that the functional margin has the following formula and I have read that given a training set we define the function margin of (w.b) with respect to S to be the minimum of this functional ...
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Only one support vector in a linear svm kernel

I am new to SVM, but I would like to understand certain things. Firstly, when dealing with multiclass classifications, I have a large number of support vectors as proven by R. However, when I run ...
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How can artificial neural networks help in designing a support vector machine?

It is common practice in artificial neural networks (ANN) courses to provide an extensive study of support vector machine (SVM). It surely is a great addition to our technical formation but I have ...
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37 views

Classifier for different classes of birds

Is it possible to distinguish between the different classes of birds? I do not aim to have a very specific classifier, but a classifier that is atleast able to distinguish between a parrot and an ...
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Visualizing SVM results

I would like to know if there are ways to visualize the separating hyperplane in an SVM with more than 3 features/dimensions. Normally, classification plots are possible with 1,2 and 3 dimensions (see ...
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Transform SVM instance to ensure zero bias

I guess the title is self-explanatory, but here it goes. Is there a way to transform the input to an SVM (i.e. the data points) in order to obtain a solution with zero bias?
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Support Vector Machine input type

whenever I search for the usage of support vector machine(SVM) for classification, I have always seen that SVM takes objects as numerical vectors. So, to use support vector machines for a ...
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Generating features: What level of interaction?

I have multi (3) level data indexed by i,j,t. As such, I can generate fixed effects (dummies) for either ij, it, or jt, (and still achieve identification). I can also do i,j,t separately as well. ...
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Features for Object Detection

I want to build a classifier for detecting Airplanes in images. It is important to note that size and shape of the Airplane does not matter in the image. So for training I might simply use the images ...
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53 views

Clustering by fitting many linear SVMs and clustering their weight vectors?

Let’s say I have a bunch of discrete sequence data, with each sequence belonging to some individual (there are ~1000 individuals and many more sequences). With a great deal of success, one can train a ...
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Lowering C parameter increases the number of support vectors

I know that the C (cost) parameter controls the trade-off between model complexity and misclassification. A large C should increase the error weight, and therefore the model complexity. Nevertheless, ...
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When using SVMs, why do I need to scale the features?

According to the documentation of the StandardScaler object in scikit-learn: For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support ...
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Machine Learning Methods for Binary Classification

I was hoping to get a nice list of alternatives to logistic regression and decision trees for binary classification ("Yes vs. No" or "Cured vs. Not cured"). I am more interested in identifying the ...
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What is “Verbose” in scikit-learn package of Python?

What is "Verbose" in scikit-learn package of Python? In some models like neural network and svm we can set it's value to true. This is the documentation: verbose ...
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How to simulate more data for machine learning?

I am attempting to analyze a small dataset using machine learning (SVM, binary problem). There are $103$ samples and $215$ variables (all variables are real numbers). Some of the variables (around ...
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SVM always predicts same label

I have 11 labels. I trained an SVM model: ...
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Is it possible to apply a weight against multiple attributes in Rattle?

Hi I'm trying to figure out how to apply weight to multiple attributes in Rattle. I'd basically like SVM or random forest models to give greater weighting to ...
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55 views

How to use SVM to do time series prediction?

I want to know how to use SVM to do time series prediction? what the differences of input vecvtor X of our model between time-series prediction and standard kernelized regression problem?
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25 views

Combining text and non-text features

I am working on a binary classification problem using SVM. I am currently using ksvm in R (kernlab package). The input is a combination of text and scores. I would like to be able to use substring ...
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Time series forecasting using SVM

I am trying to set up a Python code for forecasting a time series, using SVM libraries of scikit-learn. My data contains $X$ values at a day interval for the last one years, and I need to predict $y$ ...