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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Is SVM still an active research area?

Recently I am learning SVM classification and regression, I found that most of the work are proposed in the 2000s, (around 2004~2007), but I don't understand why people stop developing it(do they?), ...
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Does RBF-network (~Nadaraya-Watson kernel smoothing) work in high dimensions?

It seems that a single-layer RBF-network with normalized weights is the same thing as kernel smoothing (see e.g. Haykin "Neural networks: a comprehensive foundation", Section 5.12). Then - it's ...
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Why are SVMs hard to fit?

I often hear the following complaint from people: "SVMs work really well WHEN they actually work." By "work" I mean that the algorithm will actually finish running. Are SVMs difficult to fit in ...
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Why SVM struggles to find good features among garbage?

I'm work on a small data set with a many features where most of them are just garbage. The goal is to have a good classification accuracy on this binary classification task. So, I made up a small ...
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SVMs with Dummy Variables in Matlab [on hold]

Context: I have a cell array with 19 features that are all categorical (nominal) (as columns) and ~1500 data entries (as rows). I've looped through all the columns and used ...
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How to score the predictions of a classification model?

I have made a classification model using support vector machine for the classification of two classes.The model is giving probability score and decision value for the test and training set and also ...
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29 views

Binning a continuous feature for a SVM

I am using a support vector machine (SVM) for binary classification. One of my features is continuous: each item has an attribute $x$ that is a real number. For various reasons (e.g., because I ...
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Issues in Testing SMO SVM

I am new be in SVM and SMO algorithm, I implemented SMO using the pseudocode provided in : “Fast training of support vector machines using SMO” by John platt. I am finding issues testing my ...
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SVM with non-negative weights

An SVM classifier can be obtained by solving the following, $\arg\min \frac{1}{2}\|W\|_2^2 + C\sum_i \max(0, 1-y_i (W^T\mathbf{x}_i + b))$ where $W$ is the hyperplane (or weights), $b$ is the bias, ...
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38 views

How to get probability from the confidence score in SVM

In liblinear library we can get confidence score (the distance between decision hyperplane) in SVM solver for a binary classification problem, but if i want a probability value for membership in any ...
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Feature selection of SVM

My question is three-fold In the context of "Kernelized" support vector machines Is variable/feature selection desirable - especially since we regularize the parameter C to prevent overfitting and ...
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How to use Weight vector of SVM and logistic regression for feature importance?

I have trained a SVM and logistic regression classifier on my dataset. Both classifier provide a weight vector which is of the size of the number of features. I can use this weight vector to select ...
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SVM Classifier with HOG Features

I am interested in having a system to detect and recognize speed limits from traffic signs. The detection part works fine, meaning that I am able to detect them inside any image. Now I would like to ...
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20 views

SVM for more than 2 classes

I need to know how to carry out Support Vector Machines (SVM) with more than two classes. Is there a book or reference about this? Thanks in advance.
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How to classify different skeleton poses into a predefined pose or other poses using Kinect?

I'm using Kinect for windows and the input is the skeleton frame joints locations. As a result, I can track different joints locations in time. I need to detect this pose: With hands straight ...
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41 views

Class weights in caret

I'm using the R package caret to generate classifiers using a variety of different models on an imbalanced dataset. To overcome the class imbalance problem, I am using the "weights" parameter in the ...
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40 views

Weighting features prior to SVM

I'm building an object detector using HOG features and linear SVM. Some of the regions of the object are more "distinctive" so I would like to give more weight to the features extracted from those ...
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16 views

validation set with ranking variables

i'm working on an approach of feature selection with SVM model and i have some questions about validation , training and test sets. the idea is to rank variables in decreasing order of relevance ...
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30 views

Need guidance in image classification

I'm new to machine learning and need some help. I need image classification to tell if an image is a car or not. Is there any working example or guidance or a book for this particular question? ...
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Meaning of $\sum_{i=1}^n \alpha_i<n-t$ in svm? and it's primal countepart

Consider svm-dual,i.e., \begin{align} &\text{maximize} \sum_{i=1}^n \alpha_i-\frac{1}{2\lambda} \sum_{i,j=1}^n \alpha_i \alpha_j y_i y_j K(x_i,x_j)\cr &\text{subject to, } 0\leq \alpha_i ...
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How to detect classifier curve in non-separable SVM problem

Suppose we want to classify two class of data that are non-separable with hyper-plane. So we use kernels to map data to high-dimensional space. See my codes: ...
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20 views

How does linear SVMs function in multi dimensional feature space

Can someone please explain me how does linear SVMs function in multi dimensional feature space ? I'm not able to picture how a linear SVM can perform classification in more than 2 dimensions. Also, ...
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18 views

How to optimize RBF parameters $C,\gamma$ with KSVM method?

I want to find the best choice of $C$ and $\gamma$ parameters for Radial Basis Function kernel. I am using kernlab instead of e1071 library. So how can i optimize RBF parameters $C$ and $\gamma$ with ...
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48 views

Why is random forest inconsistent in text mining?

Earlier I've used SVM (rbf kernel) in text mining with success, and after that for similar text mining work with long texts I've used random forest with success as well. However in a recent kaggle ...
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How to deal with, and use, missing data (MNAR) in svm?

I am trying to predict future spending of customers based on past transactions. My data looks as follows: ...
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Precomputed kernel into the SVM

In sci-kit svm, we can pass a precomputed kernel to construct the SVM. Why is it/is it not a bad idea to pass the ideal kernel (i.e., over the train data) into the svm? The only reason I can think of ...
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Representative signal

I'm implementing machine learning with sensor data. I am having the problem that some sensors not always have good integrity, that is, not all data points arrive at destination because of ...
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maximal margin classifier (length of margin)

In support vector classifier, the separating hyperplane has an equation $x^T\beta + \beta_0 = 0$. We know that the coefficients $\beta$ are actually the components of the normal vector to the ...
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37 views

How does svm deal with new levels of a variable added over time when considering time series data?

I am trying to predict customer spending for an X year period after t0. I train an svm model with transactions occurring before and on t0, on the cumulative spending of the customers after t0. I then ...
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nonseparable case of classification problem (SVM)

I am learning Soft Margin Classification (SVM) right now. In cases when the classes are non-separable by the usual hyperplane with a margin $M >0$, we modify the constraints and say that it is ok ...
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Unconnected Linearly Seperable Classification

Consider classifying something like the case shown below (exagerated syntetic example): If this were a task to classsify into 3 groups, (blue-left, red, blue-right), then a Linear Support Vector ...
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23 views

support vector machine question on coefficients

I am a beginner at SVM and I am currently reading Introduction to Statistical Learning. Our aim is to maximize the margin of the of hyperplane boundary subject to certain conditions. My question is, ...
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29 views

Non-vector data and SVM?

My research is on antimicrobial peptide classification and prediction. I have gathered peptide sequences of lengths ranging from 10 - 200 and classified them using different machine learning ...
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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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79 views

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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78 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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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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1answer
56 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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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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32 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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1answer
10 views

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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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 ...