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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The optimization problem of soft margin Support Vector Machine: How to interpret?

I try to understand what exactly we are trying to optimize in the case of Support Vector Machine problem, which supports soft margins. The original problem is posed first as, without soft margins ...
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16 views

Grid search error in LIBSVM while optimizing C and g parameters

I am using libsvm for a one-class classification problem. I am trying to select the ideal C and gamma parameters for different kernels(polynomial, linear and rbf) I am using the suggested matlab code ...
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15 views

Distance from hyperplane in SVM rbf kernel in R

I am running ksvm in R(using kernlab package) for a highly imbalanced data.Is there any way i can get the distance of my test data points(each of dimension 8-10) from the hyperplane?so that i can ...
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19 views

One-class SVM vs NN with backprop… Or is there something better?

I'm pretty new to unary classification, so I've been playing around with different approaches to one-class document classification in Python. NN seemed promising at first, but has some undesirable ...
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17 views

Feature engineering with non-fixed length vectors?

I have a bunch of data that looks like this: ...
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6 views

max margin vs max posterior/likelihood advatages

I am working on some parameter learning approaches for image classification. What is the differences between the following two for image classification? max margin methods maximum likelihood/ ...
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10 views

Profiling high-scoring clusters in a multi-dimensional feature space

I have a large amount of samples, which have a multi-imensional feature vector associated with them. Each sample has a "score", and the length of the feature vector is substantial (n>100, and in ...
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40 views

Why SVM approach gives nasty results(Weka) [closed]

I am using weka as a data mining tool and I try different sorts of classifiers to classify my 10k instances file. With trees->Random Forest I have about a 99.2 ...
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16 views

SVM decision boundary for linearly transformed (strictly positive definite, diagonal) data points

Let the training data given as $ \left\{x_i,y_i\right\}_{i=1}^n $ and let the corresponding optimal max-margin SVM classifier be $ f\left(x\right)=w^Tx$. Let us now apply a strictly positive definite, ...
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1answer
17 views

Check a status of training process in R

I'm training a model using caret package in R for almost 3 days. The calculations are running in parallel (multiple processes). Unfortunately there is no output in ...
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35 views

Explanation for large difference in SVM and Naive bayes results

I have a dataset with 389 data evenly distributed into 6 classes. Each data has 1024 features. So my dimension is much larger than my observation data. I have tried to see some common classifiers on ...
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68 views

SVM heavily over fits the data (classifying Highly Unbalanced data )

I have a huge training set from which I am supposed to regress and classify, i.e I classify whether an event will occur or not and another task is to regress the intensity of the event in future. The ...
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1answer
14 views

Affect of Misclassification Cost on SVM

I am using Matlab to train an SVM for very unbalanced data. However, my concern is not so much for the particular class assignment (ie 1/0), but rather to the scores (the prethreshold continuous SVM ...
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1answer
44 views

SVM: Number of support vectors

Imagine I am using an SVM to train a classifier for a given dataset, in one-vs-all configuration. For each class, I am performing cross validation for parameter selection (grid search to choose ...
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38 views

Novelty and Outlier Detection in Unsupervised Learning Style

Currently I am looking for some method to do novelty and outlier detection. I found some good example here using scikit-learn (Link1). However, it is based on supervised learning and I believe the ...
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1answer
22 views

Novelty and Outlier Detection for Multi-label Data

I met a problem of using novelty and outlier detection for my multi-label data. For example, I have got some training data that is not polluted by outliers. However, the training data are with ...
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1answer
36 views

Plotting decision boundary of Logistic Regression (liblinear)

I have liblinear model file for a classifier learned using logistic regression. In the file, they say, the weight vector and intercept term. But when I simply plot it as $$w^Tx + b$$ on the original ...
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22 views

Kernel SVM on sparse data

I have a sparse dataset where a lot of the columns (features) contain mostly zero values. Class labels are multiple discrete categories (10 classes to be precise). I'm wondering if this should trouble ...
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14 views

Binary classifier issues

I am trying to predict if sales are going up or done given a specific set of features. The only thing I care about is precision here. In this context I tried a few classifiers ( SVM, Random Forest ) ...
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19 views

SVM predictions of timeseries (forex) data are shifted

I am trying to build timeseries prediction SVM (regression variety) for forex data based on lagged close data. And I am using R. Please see the simple code below and resulting graph, using e1071 ...
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21 views

multiclass classification and unbalanced dataset

I have a five-class SVM multiclass problem. The dataset is small (about 160 examples) and unbalanced i.e. I have classes with few examples. So far I further limited the dataset to 110 examples in ...
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30 views

Possible overfitting?

Hi I have a limited dataset with 100 examples with 15 features. I trained a linear svm with 80 samples after I did a 5-fold cross-validation and found the best parameter values for C. Then I tested ...
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6 views

Are there closed-form expressions providing the VC-dimension for the multi-class case for different classifiers?

So far, I've only encountered the VC-dim for binary classifiers. I'm interested to know how this notion can be extended to the multi-class case. Are there expressions that provide bounds on the ...
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21 views

Using kFold in a regression problem?

Can I use kfold crossvalidation in a regression problem? The best MSE found in a kFold has poor results in the test set, than other ones (like e-SVR with default parameters in libsvm).
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37 views

Spatial coordinates (latitude and longitude) are non significant

I want to use latitude and longitude as a feature for models like SVM or Logistic Regression (both for classification). What is the most common approach to use latitude and longitude values as ...
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5 views

find optimal parameters for SVM from tune() in R= [migrated]

I am optimizing the gamma and cost variable using the tune function ...
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10 views

assign asymmetric cost in SVM classification in R?

I know that we can set a symmetric cost in R for svm,how do we set an asymmetric cost? I want to do a grid optimization like the following: ...
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47 views

In soft-margin SVM, is it guaranteed that some points will lie on the margin?

In soft-margin SVM, once we solve the dual-form optimization problem (using quadratic programming, which is guaranteed to converge on a global optimum because the problem is convex), we get a vector ...
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11 views

kFold and defaul gamma in libsvm

I have a 1600x8 value table, trying to do a SVR. After a few validations, c=2048/gamma=2 have the smallest MSE in cross validation. With this parameters, the test part was not good as C=2048, and a ...
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29 views

How to find optimal penaltyparameter C for SVM (regression)

I am training an svm regressor using python sklearn.svm.SVR From the example given on the sklearn website, the above line of code defines my svm. ...
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1answer
35 views

SVM parameters clarification

James et al. in An introduction to the statistical learning (p. 351) claim that the solution to the support vector classifier problem involves only the inner products of the observations. They ...
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65 views

Output of Scikit SVM in multiclass classification always gives same label

I am currently using Scikit learn with the following code: ...
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57 views

Why does the linear SVM give a lot of support vectors?

I simulate a simple linear setup: n = 1000 X = runif(n) Y = runif(n) ind = X + 2*Y < 1 ind[ind == TRUE] = runif(sum(ind)) < 1 plot(X,Y,col = ind + 1) ...
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87 views

PCA on train and test datasets: should I run one PCA on train+test or two separate on train and on test? [duplicate]

I'm doing an image classification task and the number of features of each example image is pretty huge (3,072: # pixels in each image). I'm thinking of using PCA to reduce the # features of each image ...
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1answer
47 views

Issues plotting a fitted SVM model's decision boundary using ggplot2's stat_contour()

I'm trying to figure out how to plot a decision boundary for a fitted svm model in ggplot2. Right now, I'm attempting to do so by using stat_contour. Here is my code with an example call to my ...
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28 views

SVM Dual Formulation :: KKT Constraint

In Andew Ng's SVM course notes, the final hard margin optimization problem is given as the following: I am unclear how to see from this where the 5th constraint is satisfied. The definition of ...
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25 views

Support vector regression in weka

I am using SVR for statistical down-scaling of precipitation. I have taken the first 3 factor scores in principal component analysis of variables as predictors and precipitation as predictand. As ...
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1answer
53 views

In natural language processing (NLP), how do you make an efficient dimension reduction?

In NLP, it's always the case that the dimension of the features are very huge. For example, for one project at hand, the dimension of features is almost 20 thousands (p = 20,000), and each feature is ...
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1answer
21 views

Structural risk minimization and SVMs

I know what is SRM but I didn't understand the relation between SRM and SVMs. Can anyone explain me this? Why they say that SVMs rely on a SRM approach? Thank you so much!
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25 views

Intution on Interchangability of Regression and Classification

Dear Oracles of CrossValidated, I've been trying to gather intuition on the relationship between methods that seems to be escaping me. Can someone explain how regression and classification can be ...
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18 views

Predicting the near-future values using an unevenly sampled time-series data

Summary Need help with predicting the near-future values using an unevenly sampled time-series data. Data is collected as events, and is converted to time series. I have tried out a few approached ...
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9 views

How to deal with clustered features in classification

Imagine there are three classes of data, labeled A,B and C. I have separated the train set ...
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1answer
48 views

The role of $\gamma$ & $C$ in SVM

I'm using support vector machine method with the Gaussian kernel. Is it true that $\gamma$ and $C$ are hyper parameters of SVM? What is their role exactly?
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1answer
57 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
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21 views

Using SVD on features before SVM classification, when p >> N

So I am going through Hastie's Elements of Statistical computing, and in section 18.3.5 which deals with computational shortcuts when the number of dimensions $p$ is much larger that the number of ...
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1answer
26 views

Label propagation in semi-supervised learning

Suppose we have a set of labeled and unlabeled instances. 70%unlabeled 30% labeled. We apply a semi-supervised algorithm. Let's say we apply S3VM or Laplacian SVM. We use all the data available. When ...
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15 views

Optimal Margin Classifer : Optimization Problem Setup

In the notes from Andrew Ng Machine Learning course, he writes the initial optimization problem as follows. I am confused by the notation and suspect I am missing something simple. Given the ...
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12 views

Do you know about SVM plait?

I need to know about how I can applied many single SVMs? because I have read about SVM plait that does this kind of classifications that is using many single SVMs to improve the classification process ...
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1answer
60 views

R caret package - number of principal components when preprocessing using PCA

I am using the caret package in R for training of binary SVM classifiers. For reduction of features I am preprocessing with PCA using the built in feature [preProc=c("pca")] when calling train(). How ...
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74 views

How does Support Vector Machine compare to Logistic Regression?

Support Vector Machine (SVM) and logistic regression (LR) have been discussed widely in machine learning community, I know that both of them achieve pretty good performance. But, I am not sure how in ...