Questions tagged [svm]

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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Robust Support Vector Regression - robust to outliers

I've been reading/looking around for literature on support vector regressions that are relatively robust to outliers. I understand that standard SVRs can be significantly influenced by a few large ...
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Using Conditional Optimizations in Training Algorithm of Linear Support Vector Classifier

I am trying to come up with an easier algorithm than usual nonlinear optimization algorithms for linear support vector classifier. My idea is Once classifier is determined, it's easy to decide ...
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1answer
4k views

Using SparseM/Matrix Sparse Matrix in training SVM from e1071 returning different results from same data in standard matrix

Using sparse matrix objects in svm training in e1071 returns different results than running on the same data represented as standard matrix: ...
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2answers
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Finding the Bayesian classifier for a bivariate Gaussian distribution

Very close to: Joint Gaussian of two Gaussians I am trying to find the Bayesian classifier for two classes given by the following bivariate Gaussian distributions: $$p(x|c_1) = N(\mu_1, \Sigma_1)$$ $...
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1answer
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Grid Search for Polynomial Kernel

I am trying to do a grid search for a polynomial kernel. I have used the normal grid search on polynomial kernels which is wrong. I only varied $c$ and $\gamma$... but I need to vary $a$ and $b$... Is ...
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1answer
370 views

SVM parameter selection with NM simplex (or other algorithms)

I'm having some trouble getting the NM Simplex to find a good minimum for selecting hyperparameters of a rbf SVC. Not only am I tuning the 2 SVC parameters (C and gamma) I also have five class weights ...
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2answers
3k views

How to explain poor classification performance of recall when using SVM?

I applied SVM to perform the classification against several data sets. It turns out that the performance metric of recall is pretty bad for one data set. It has recall around 50% while other data sets ...
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1answer
510 views

Non-linear regularized SVM implementation

Just a general question. Are there any good non-linear SVM (kernelized) implementations that include a regularization component (e.g. $L_1$, SCAD etc)? I've been looking around but man there are a lot ...
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1answer
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How to select the final model with elastic net feature selection, cross validation and SVM?

I have a dataset of some 100 samples, each with >10,000 features, some of which highly correlated. Here's what I am doing currently. Split the data set into three folds. For each fold, 2.1 Run ...
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262 views

Non-linear (e.g. RBF kernel) SVM with SCAD penalties implementation

Is there one? I think there's a penalizedSVM package in R but it looks to use a linear kernel. Can't quite tell from the documentation. If it's linear, is there a R package that lets me calculate the ...
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1answer
126 views

General Non-linear Regularized Models

Had a general questions. Are there any good non-linear models with regularization? I've heard of some linear models with regularization but not too many non-linear ones. I understand that you can use ...
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3answers
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Train NN or SVM to classify stock signals

I am applying Neural network and SVM to predict buy-hold - sell signals. I have trained nn and SVM in R. I used nnet function to train NN and svm to train SVM. I provided 20,000 data points to train ...
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Parameter Search for SVM on the whole data

I am trying to implement SVM and i did my parameter selection(grid search) on the whole data and used the best values of C and gamma from that search to test on the testing data. Sometimes, the cross-...
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1answer
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Does a sparse training set adversely affect an SVM?

I'm trying to classify messages into different categories using an SVM. I've compiled a list of desirable words/symbols from the training set. For each vector, which represents a message, I set the ...
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1answer
346 views

Probabilistic outputs from SVMs

I remember a paper from 1999 (13 years ago!) called Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods (1999) by John Platt that outlined a method for ...
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1answer
559 views

Multiclass SVM + Ineffective X Validation, Time Series Prediction

I've recently run into an interesting and rather odd problem with cross validating a multiclass SVM that I can't figure out. Basically, I have a timeseries to predict and have created a dataset of ...
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1answer
197 views

SVM and cross validation with a minimum finding algorithm

Just a simple question on parameter selection for SVMs. If I use a minimum finding algorithm to find the optimal parameters for a set of data, how do I "average" the parameters over a set of cross ...
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Building document exemplar training models for SVM

What are the best methods for building document exemplar training sets for classification of unstructured data (documents and emails) using SVM? How do I optimize F-scores for these models when using ...
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5k views

SVM, variable interaction and training data fit

I have 2 general/more theoretical question. 1) I'm curious how SVMs handle variable interactions when building predictive models. E.g., if I have two features f1 and f2 and the target depends on f1, ...
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Best way to handle unbalanced multiclass dataset with SVM

I'm trying to build a prediction model with SVMs on fairly unbalanced data. My labels/output have three classes, positive, neutral and negative. I would say the positive example makes about 10 - 20% ...
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1answer
156 views

Is there any sense of adding the same feature with opposite sign when using SVM?

I'm training a support-vector machine (SVM). Each training vector includes 2 features which are equal in magnitude and have opposite signs, i.e., $F_1 = -F_2$. Is there any sense in doing so? Is one ...
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1answer
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Differences and connections among different machine learning methods

For the following popular data mining methods: SVM, neural network, logistic regression, random forest, classification tree, Naïve Bayes classifier, regression; How to compare them in terms of ...
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Matlab's quadprog equivalent in python? [closed]

For my SVM algorithm I need to do an optimization in standard QP form. In Matlab I would use 'quadprog' with the 'interior-point-convex' algorithm from the Optimization toolbox. What is an ...
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How well does R scale to text classification tasks? [closed]

I am trying to get upto speed with R. I eventually want to use R libraries for doing text classification. I was just wondering what people's experiences are with regard to R's scalability when it ...
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Classification score: SVM

I am using libsvm (which is meant for solving binary classification problems) for multi-class classification. How can I get classification scores / confidences for each class to effectively compare ...
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3answers
9k views

Using an SVM for feature selection

Lets say I have a highly dimensional classification problem with a lot of noise, and I want to improve my results by removing some of the noisy variables. I've read several papers on using SVMs for ...
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1answer
726 views

What do “real values” refer to in supervised classification?

I'm using supervised classification algorithms from mlpy to classify things into two groups for a question-answering system. I don't really know how these algorithms work, but they seem to be doing ...
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1answer
865 views

Information on how value of k in k-fold cross-validation affects resulting accuracies

I've been doing some Machine Learning, and have been using k-fold cross-validation to assess the generalisation performance of the algorithm. I've tried k-fold cross-validation with k = 5 and k = 200 ...
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1answer
362 views

Can anyone explain why I have obtained an anti-predictive Support Vector Machine?

I'm playing with support vector machines (SVM) using the e1071::svm() function in R, and I encountered a scenario where I asked it for a leave-one-out cross-validated classification of a 2-category ...
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1answer
1k views

Dual problem for L2 support vector machine

Here is the dual problem for L2 support vector machine: $$\max_{\alpha\in\mathbb{R}^{n}} 2\alpha^{T}y-\alpha^{T}\left(K+n\lambda Id_{\mathbb{R}^{n}}\right)\alpha$$ $$\forall i\in\left\{ 1,\ldots,n\...
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Efficient way to classify with SVM

I'm doing a binary classification using SVM classfier, libsvm, where roughly 95% belongs to one class. The parameters C and gamma are to be set before the actual training takes place. I followed the ...
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1answer
2k views

The difference between linear SVM and other linear classifiers?

The linear SVM in textbook takes form of maximizing $L_D = \sum_i{a_i} - \frac{1}{2}\sum_{i,j}{a_ia_jy_iy_jx_i^Tx_j}$ over $a_i$ where $a_i \geq 0$ and $\sum_i{a_iy_i} = 0$ Since $w = \sum_i{...
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Reading in SVM files in R (libsvm)

The data files from http://www.csie.ntu.edu.tw/~cjlin/libsvm/ are in 'svm' format. I am trying to read this in to sparse matrix representation in R. Is there an easy/efficient way to do this? Here ...
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1answer
1k views

Should an SVM grid search show a high-accuracy region with low accuracies around?

I have 12 positive training sets (cancer cells treated with drugs with each of 12 different mechanisms of action). For each of these positive training sets, I would like to train a support-vector ...
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2answers
1k views

Few machine learning problems

In a particular application I was in need of machine learning (I know the things I studied in my undergraduate course). I used Support Vector Machines and got the problem solved. Its working fine. ...
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2answers
324 views

Heuristics for optimizing ν-SVM?

Do you know any good heuristics for finding optimal value of ν in case of ν-SVM classification? In this particular problem I have a radial basis kernel, if it helps.

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