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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Matlab GUI background color changing to black when using parallel computing for MLP and SVM

I designed a GUI in Matlab that uses parallel computing in loops for accelerating speed. When I disable parallel computing everything Is normal but when I activate it background color of my GUI and ...
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20 views

What´s the fastest SVM library for C# [on hold]

I want trainning my nets faster. I´m using KMLib (not GPU), but the speed is not so good. My problem is a classification one.
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110 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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1answer
16 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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3answers
43 views

Is LDA more likely to be overfitted than SVM?

I went to a short talk and the speaker quickly mentioned something like 'LDA is more likely to be overfitted than SVM'. Is this true? And why? Thanks a lot. A.
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22 views

Logistic regression with multi-class features in R

I'm working with a data set like the following: X = ...
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1answer
61 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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3answers
26 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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26 views

Hyperplane data points

Would a single data point in the hyperplane (see below) correspond to a single cell in the data matrix or an entire row?
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1answer
25 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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1answer
49 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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1answer
36 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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1answer
12 views

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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1answer
24 views

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 ...
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22 views

Scaling in SVM (why and how to , plus references)

Hi I know why feature scaling is preferred in SVM, I have two questions: 1-does anyone know of legit articles of books explaining it. I am writing my thesis and I need references. It doesnt have to be ...
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14 views

Solution to the SVM problem

in support vector machines the idea is to find a decision boundary in which the margin is maximized. This can be written as $$ \text{minimize} \ \lVert w \rVert$$ $$\text{subject to} \ \ ...
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8 views

Achieving high recall for smaller class in unbalanced linear svm

I have an svm-related question. I have an unbalanced dataset, meaning classA could be 1/10 to 1/35 of classB. Well I am interested in getting a linear svm which would separate the data and would ...
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2answers
32 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
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28 views

Interpretation of Linear SVM Coefficients [duplicate]

I’m building a model using Linear SVM from the Scikit-learn package in Python. I have found that Linear SVM performs much better on my training set than Logistic Regression. My question is, is there ...
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20 views

Setting up feature vectors

I am working on a classification project and I want to use SVM's and/or Clustering Algs. What I am having trouble with is deciding how to set up my feature vectors. I have already decided what my ...
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11 views

SVM Numerical example (step by step)

I have a constant problem understanding SVM for both linear and non-linear separable cases. I understand upto a point that SVM establishes a hyperplane that has the maximum or optimal distance between ...
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1answer
35 views

Finding optimal hyperplane

I have a set of vectors $\{V_i\}$ in $n$-dimensional space. There is a number corresponded to each vector $\alpha_i = f(V_i)$ ($\alpha_i$ can be negative). I want to find a hyperplane which would ...
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1answer
25 views

Unbalanced test data matter?

I have balanced training data: 300 positives and 300 negatives, but the test data is unbalanced: I have 15 positives and 60 negatives. Will the unbalanced test data impact classification accuracy? ...
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1answer
44 views

How do support vector machines avoid overfitting?

I understand that in the dual form of the model for support vector machines, the feature vectors are expressed only as a dot product. Mapping the feature vectors to a higher dimensional space can ...
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13 views

Missing data and SVM

I know that in multiple imputation, one runs a regression for each imputed data set. The final regression coefficients are averages of the coefficients obtained from the imputed data sets. Can you do ...
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9 views

How to interpret merits in Weka with ChiSquaredAttributeEval and SVMAttributeEval?

I want to interpret the goodness of attributes using feature selection with 10-fold cross validation. With ChiSquared I get something like this (deletet attributes with merrit was 0 in all folds): ...
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20 views

Predict feature combination with highest probability

I trained a Support Vector Machine with the caret package in R. My dataset looks the following: ...
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6 views

Different Scoring with libsvm

I have been using libsvm for binary classification, e.g. given data $(x_1,\dots,x_n)$ and labels $(y_1,\dots,y_n)$ where $y_i\in\{0,1\}$. Now I am wondering if the prediction of my model can be tuned ...
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30 views

SVM cost parameter

In a SVM with linear kernel, could you explain to me what exactly the C parameter is/represents? An example why it's important to select a good value for C would also be appreciated. Thank you.
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18 views

Support Vector Machines and Missing Values

Suppose a data set has the following binary values for the variable z: z x y 1 0 1 1 0 1 The variables are x and y. The training data set consists of the ...
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19 views

Cross Validated Squared Correlation Coefficient in SVM regression

I am applying SVM regression on a data set on "Karthikeyan Melting Point Dataset" present here. I am using LIBSVM for it. After applying regression LIBSVM returns the cross validated squared ...
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41 views

Predicting the Success of a tweet

I want to predict the success of a tweet. In my case a tweet is successful when the sum of the number of favorites and the number of retweets is greater than 5. So my outcome value y is: y= ...
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1answer
51 views

Feature selection : how to select the Information Gain threshold?

I am trying to use Information Gain to select features when classifying text with a Support Vector Machine. For each word in our training data, we computed its information gain. Then, we should keep ...
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25 views

Using LibSVM regression to predict dependent variable

My intention is to use SVR with 1-10 independent variables to best predict a dependent variable. After separating the data into scaled training and testing files containing the independent variables ...
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1answer
13 views

How to increase a particular terms's weightage?

I am doing Text classification using LibSVM in Rapid Miner. I am using TFIDF values for processing documents. I need to Increase weightage of some terms in the documents(for eg. words in BOLD and ...
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1answer
46 views

Extracting decision function variable from libsvm

I'm trying to use LIBSVM's single class SVMs for some classification and need to extract the following sum post classification (i.e. the variable that the decision function takes in) $$ ...
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1answer
25 views

Why are linear SVMs used with HoG feature descriptors?

Ok, almost all applications I have seen that use HoG features use linear svm as classifier. Can someone explain for me why linear svm are chosen and why they give good performance? Are linear svm ...
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1answer
31 views

Rule from SVM results

I am using Support Vector Machines from "e1071" package in R. For standard IRIS data the code is: data(iris); attach(iris); x <- subset(iris, select = -Species); y <- Species; model <- ...
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24 views

How to implement data I have to svmtrain() function in MATLAB?

I have to write a script using MATLAB which will classify my data. My data consists of 1051 web pages (rows) and 11000+ words (columns). The first 230 rows are about computer science course (to be ...
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6 views

Nonlinear functions of other features as new features in SVM model with RBF kernel

Can some one give me some conceptual insight on the potential advantages of disadvantages of adding features that are (nonlinear) functions of existing features in training an SVM model with an RBF ...
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23 views

One vs All appraoch to multiclassification in LIBSVM

I'm working on multiclass classification problem (precisely 4 classes). I want to use the most simple approach for this problem: one-vs-all! I have 4 different test set (only labels are different). ...
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2answers
57 views

Plotting the decision boundary of a kernel SVM (RBF)

Suppose we are given a training set of 2D points that are linearly non-separable. I train a binary SVM with an RBF kernel in order to classify them. What I want to do is to draw the desicion boundary. ...
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23 views

What are some linearly inseparable data sets for testing support vector machines and artificial neural networks?

I am looking for some linearly inseparable classification problems and nonlinear regression problems. What are some public data sets?
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22 views

STRING KERNELS FOR LIBSVM

I'm working on a protein classification problem and i'm using edit distance kernel defined in libSVM. Now, for instance, the implementation of spectrum kernel is very difficult, but i want try to test ...
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23 views

Unstable models, repeated crossvalidation, feature selection

I'm still trying to classify few (about 200) samples in a high dimensional feature space (dim=19) into 3 (very unbalanced) classes. I use an implementation of Least Squares SVM with one vs one coding ...
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1answer
38 views

e1071 svm predicted probabilties are all 0.5

My svm classifier model always predict 0.5 as probabilities. ...
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12 views

In R, how to predict with svm model in parallel using foreach/snow? [migrated]

I'm trying to improve the performance of my R program, which is using an SVM trained on PCAs, by using the foreach and doSNOW packages. I've already trained the models and am now passing my validation ...
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2answers
30 views

Evolution strategies in libsvm

I'm working on protein multi-classification problem. I'm using libsvm and the edit distance kernel. This kernel depends from a parameter (gamma). I'm able to get the best parameters (gamma and C) ...
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1answer
48 views

Reverse AUC interpretation

Given a classifier (SVM) classifying in 2 'classes' (+1 or -1) for prediction purposes. It has an AUC score of 0.28, meaning its success rate is lower than just random predictions. If I just do the ...