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 is SVM calculated in this way?

I have a question regarding SVM. I understand the Lagrange equation. $L(w,b,\alpha) = \frac{1}{2}w'w - \sum_i \alpha_i (y_i(w'x_i+b)-1)=$ $\frac{1}{2}w'w - \sum_i \alpha_i y_i(w'x_i+b)+ ...
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How to perform multiclass SVM classification using k-fold cross-validation and SMO with some kernel method in MATLAB?

I have data matrix X.csv file of size nxd, where n are the observations, and d variables. There are c_1,..., c_m classes. Let, Y be the matrix containing the class labels. There is no header row in ...
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Equal Error Rate (EER) and Receiver Operating Characteristic (ROC) curve

I have a one-vs-all classifier set. This set consists of, let's say, 3 classifiers (LibSVM SVMs) each trained on data for a class and all other class data. The current setup for a sample is that the ...
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SVM optimality criterion in Bottou, Lin (2006)

My question relates to an alternative optimality criterion for an SVM dual solution derived in Bottou, Lin (2006) in pages 8 and 9. Let: $\alpha^* = (\alpha_1^*,\dots,\alpha_n^*)$ be a dual ...
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Derivation of Support Vector Machine

I actually understood the derivation behind support Vector Machine but I have a doubt about constraint equation. Why we have a constraint equation $\geq1$ if $y_i=1$ and $\leq-1$ if $y_i=-1$? Can ...
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14 views

Cost-sensitive SVM with sklearn

Is there a direct cost-sensitive implementation of the SVM classifiers (CS-SVM) within the sklearn module? There are several ad hoc methods for the cost-sensitive SVM on "the market", but I am ...
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21 views

parameter tuning using nested cross validation

Parameter tuning in SVM has been performed using a nested cross-validation(CV) approach with 45 folds(outer loop) and 13 folds(inner loop). In this process, the outer loop will have 45 prediction ...
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28 views

SVM cost function: old and new definitions

I am trying to reconcile different definitions of the soft-margin SVM cost / loss function in primal form. There is a "max()" operator that I do not understand. I learned about SVM many years ago ...
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54 views

logistic regression vs support vector machines

I can understand the logistic regression depends on entire data and support vector machines depend on support vectors, but could not understand when and why should I use svm or logistic regression. ...
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Parallelizing SVM

I have a big dataset and implementation of SVM (+ SMO) doesn't support training whole dataset at once, so I have partitioned the dataset into around 20 sets and now its successfully training on all ...
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15 views

plot accuracy of classification in Random forest and SVM in R

I have a question and I will be grateful if you help me. Is it possible to plot admixture or pure of individuals with Random forest-SVM methods (models) in R . Is there command for this work?
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7 views

RBF SVM image classification with missing features

I have been working on a image classification problem (face recognition especifically) and my test set has some missing values: for some face images only the upper half is avaliable and for others the ...
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17 views

Differences between categorical classification algorithms

Given data where the class is categorical (finite and discrete), there are multiple ways to come up with a classifier. One could use multinomial logistic regression, or support vector clustering ...
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66 views

SVM model training set vs test set

I am trying to train an SVM model using Forest Fire data. I split up my data into a test and training set. I am fairly new to this type of analysis but I'm not sure what role the test data plays or ...
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12 views

optimizing 2 factors - Can you use NSGA-II to optimize this?

I have 3 machines A, B and C. I would like to rank the machines based on which machine maximizes Score1 and Score2. Score1 and Score2 are performance measures that rang from 0-100%. Below is some ...
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Are VC dimensionality and dimension of kernel used in SVM related?

Are VC dimensionality and dimension of kernel used in SVM related to each other? or are they independent parameters in a classification process ?
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29 views

Understanding recurrent SVM in volatility estimation of GARCH model

I read Chen et al. "Forecasting volatility with support vector machine-based GARCH model" (2010) where they implented a recurrent SVM procedure to estimate volatility by a GARCH based model. The ...
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1answer
65 views

How would you represent this one-vs-all SVM accuracy?

I have a set on one-vs-all SVMs. Let's say I have three classes. I want to show FAR and FRR from the system, but I appear to get getting very large FRR values and very little FAR values. This is ...
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57 views

ROC Curve for different classifiers

I am trying to compare the classification performance of different classifiers. So far, I am using SVM, Random forest, Adaboost.M1, and Naive Bayes. 70% of data is used as training (and then plotting ...
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28 views

Why training error > 0 on SVM with RBF kernel

When using RBF kernel I think feature space is infinite dimensional space. With infinite dimensional features, I believe any training set can be classified. So I'm wondering why training error > 0 ...
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Which Algorithm and the Steps to be used for a “Product Brand Classification Problem”

I have a product dataset which contains just 3 fields, product_title, brand_id and category_id (in order). The problem is to identify the brand_id, using the other features (product_title and ...
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9 views

Scaling variables for support vector regression for time series prediction

I am confused how to best scale variables for support vector regression for time series prediction. I want to predict the next value for a time series using past values of the series (e. g. the 10 ...
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Tweaking SVM error rates?

I currently have a one-vs-all SVM setup. Each SVM outputs a score. If I take the maximum score as the correct corresponding class, this gives me FARs of 0.008%. However it also gives me FRRs of about ...
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32 views

Handling imbalanced datasets and misclassification costs in SVMs?

I have a dataset with 50 times more negative examples than positive ones. Currently, I am using an oversampling technique to address the imbalance problem. During the model selection stage (i.e. ...
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59 views

Decreasing the number of negative examples with respect to the positive examples produces good prediction with SVM

Inroduction We have a binary classification problem and we want to learn a linear SVM (support vector machine). The dataset is composed of: 502 positive examples 5020 negative examples An example ...
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What constraints to use for weights in a weighted kernel?

I want to use a Support Vector Machine classifier with the following weighted RBF kernel: $K(x,y) = exp(-\gamma \sum\limits_{i=1}^n w_{i}(x_{i} - y_{i})^2)$ There is one weight for each feature. ...
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15 views

Does LibSVM use Platt Scaling?

I have a binary SVM. I am wondering whether the percentage results that LibSVM gives for each class are Platt scaled?
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47 views

Trouble from Time series and Support vector regression (Overfitting problem)!

Why I select the SVMR because It has shown its great advantage in small sample learning and do not require stationary process. However, I have trouble doing support vector regression for a month. Even ...
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26 views

Yet another unsupervised SVM

In a typical supervised learning problem, one observe $(X,Y)$ where $Y$ is a categorical variable. We confront the such a problem that $Y$ is hidden and instead $(X,Z)$ is observed where both ...
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41 views

Modelling Periodic Features

I am trying to do a simple regression (either polynomial or support vector regression) for solar power prediction, as a project to learn machine learning. However the data I have is time series data ...
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42 views

Which classifier to use? [closed]

I have a course project that I need to finish. I'm using Weka 3.8 and I need to classify text. The result needs to be as accurate as possible. We received a train and a test .arff file. We need to ...
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43 views

Deriving the intercept term in a linearly separable and soft-margin SVM

I have read Andrew Ng lecture notes on Support Vector Machines as well as the notes from MIT OpenCourseWare and I have a few doubts concerning the derivation of the intercept value: First, there is ...
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17 views

Parameter ranges for sigmoid and polynomial kernel

I would like to use a SVM classifier with a sigmoid and polynomial kernel. The sigmoid kernel has the following form: $K(u,v) = \tanh(\gamma * u'v + \text{coef}_{0})$ The polynomial kernel has the ...
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24 views

Support vector regression (LIBSVM) returns out of range outputs when I use out-of-sample data to predict one step ahead (MATLAB)?

I'm using SVR model in MATLAB R2016a using this option: options_z = ['-q -s 3 -t 2 -c ', C_param, ' -p ', epsilon, ' -g ,Kernel_scale]; I'm optimizing SVR ...
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WEKA / Support vector machine - does attribute have positive or negative impact?

I'm doing some machine learning with WEKA using around 20 numeric attributes to predict a true/false class. Support vector machine gives me good enough results on my data set. However, I'd also like ...
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29 views

Parameter selection and k-fold cross validation

I have one dataset, and need to do cross-validation, for example, a 10-fold cross-validation, on the entire dataset. I would like to use radial basis function (RBF) kernel with parameter selection ...
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34 views

Multi-class SVM Calibration

Say we have multiple SVMs used in a one-vs-all approach, such that classes a, b, c correspond to 3 SVMs trained positively on the class and then negatively on all ...
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How to add hard negatives to original training data?

I have 2 class binary classification problem with original training data of size N=n_pos+n_neg in general case n_pos!=n_neg but now we can assume that number of positive and negative examples near the ...
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How to mitigate the hierarchical error propagation in tree-structured classification

Suppose we have a multi-class classification problem, where the number of classes $K \geq 3$ We use a tree structure of multiple SVMs to divide and conquer the problem, with one example in the figure ...
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39 views

Can we compare classifier scores in one-vs-all/one-vs-many?

In a system where we perform multi-class classification via a one-vs-all technique, are two scores comparable? E.g.: If I have 0.5 and 0.6 on two different classifiers, is it possible to say that the ...
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29 views

How to compare a Log-Log regression models with a Support Vectors Machine model (SVM)?

I have developed a log-log model which gives me a rmse of 0.1. I want to compare the results with a SVM model. In the SVM i didn't initially use the log transformed variables. RMSE from the ...
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Is output data normalization necessary in SVM regression?

We talk a lot about input data normalization, I want to know if output data normalization can do good to SVM regression, for example, maybe it could help to reduce grid search scope when doing ...
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How are datasets labelled for SVM classifier testing?

I am working on a time-series of stock prices, and want to try an SVM classifier based on technical analysis indicators (such as macd, rsi etc.) to predict whether the market situation is bullish or ...
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24 views

How to create training set for uni-variate prediction using SVM?

I am new to R and statistics. I have a problem related to the prediction: I want to predict a univariate time series using SVM, but I do not know how to construct the training set. what I want is that ...
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Are there kernel-based one-class sparse kernel-based outlier detection methods, e.g. one-class Relevance Vector Machine?

I have a commercial outlier detection problem in moderate dimension (8-25). We have a limited number of true positive tags and can roughly evaluate performance of various methods. So far, the ...
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76 views

One-vs-many/One-vs-all - what value to use as probability?

I have constructed SVMs to do a one-vs-many approach to classification. Let's say I have 3 classes and I train 3 SVMs in a one-vs-many format. This gives me 3 SVMs each trained positively on one of a ...
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fast way to train a classifier on different but overlapping features

I am training a linear classifier repeatedly on different set of overlapping features. I have a 3D grid of features, each time features from a small sphere from a grid are used to train a classifier, ...
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Overfitting in One-Class SVM

In a one-class SVM model, would a low value of $\nu$ be considered over-fitting the model or would a large value of $\nu$ be considered over-fitting? I'm very confused as, in the latter case, as $\nu ...
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How to use Cross_validation output of svm-train?

I am getting very poor values with a certain data set I have. I tried to use the -v option of svm-train but later realized that this does not produce any model file for prediction. So what is the ...
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hyper parameter optimization grid search issues

I keep running into the same problem while doing a grid search to optimize the C and gamma parameters of an SVC. Every time i do the grid search, the best values seem to occur at around C = 100000 ...