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 my simple implementation of sub-gradient descent for SVM not converging?

As an exercise in understanding the mechanics of the Support Vector Machine, I am attempting to implement the SVM myself, in Python. I'm more concerned with understanding than efficiency, so I wish to ...
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Using the mapping function to solve a problem

I am supposed to use the mapping function to solve the following graph. I understand everything perfectly fine and that you only move the points if they are greater than 2 according to this ...
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plotting SVM in python [on hold]

I tried following the example here but i am having trouble applying it when i have 16 features. lin_svc is trained with those 16 features (i deleted the line to ...
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Clustering on SVM results?

I have a data set with many subjects. Within each subject, I've run linear SVM to classify two types of stimuli that they see. The decision boundary is different for each participant, as would be ...
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88 views

SVM why do we maximize 2/||w||

If you open any SVM guide you will see that 1/||w|| is proportional to margin size (which is meant to be maximized by SVM). But how did you get this result? On the picture below you may see 2 plots. ...
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33 views

Does removing mildly correlated features (0.5) improve performance in predictive models? (SVM, random forests)

I am trying to model a binary response using a 500+ dataset. I already removed many non useful features in order to reduce dimensionality and improve my model. I am wondering whether in general ...
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382 views

Why do Convolutional Neural Networks not use a Support Vector Machine to classify?

In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art for object recognition in computer vision. Typically, a CNN consists of several convolutional layers, followed by ...
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Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible

I am trying to understand the intuition behind kernel SVM's. Now, I understand how linear SVM's work, whereby a decision line is made which splits the data as best it can. I also understand the ...
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19 views

Where can I read about gamma coefficient in SVM in scikit-learn?

Scikit learn support vector machine algorithm have a couple of coefficients which meaning I can not understand. gamma : float, optional (default=0.0) Kernel coefficient for ‘rbf’, ‘poly’ ...
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10 views

Core vector machine implementation

I came across the following article : http://www.jmlr.org/papers/volume6/tsang05a/tsang05a.pdf, Core Vector Machines: Fast SVM Training on Very Large Data Sets. The approach looks very promising, ...
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24 views

Using SVM when kernel is simple and sample size is large

Consider SVM classification: $y_i \in \{+1,-1\} $ are labels, $\mathbf{x}_i$ are covariates ($i=1\ldots N$). Let $K(\cdot,\cdot)$ be the kernel function, whose corresponding feature mapping is ...
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80 views

Practical Question about the Assumptions of Support Vector Machines

As far as I know, the only assumptions of support vector machines are independent and identically distributed data. I am planning to train and run a SVM on a number of variables that aren't naturally ...
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24 views

Getting distance of points from decision boundary with linear SVM?

I posted this originally in Stack Overflow but realize it might be more of a statistics question. I am using SKLearn to run SVC on my data. ...
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18 views

Understanding the One-Vs-The-Rest classifier

Introduction I am working on a multiclass classification problem by using the One-Vs-The-Rest classifier. I want to check if my understanding of the classifier is correct. The ...
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1answer
24 views

Confidence interval for expected prediction error from cross-validation

I am using a support vector machine for binary classification on a sample of size 150 (75 of each class). I am using 5-fold stratified cross-validation to estimate the expected prediction error, i.e. ...
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1answer
31 views

One class SVM with caret in R using cross validation

I am using one class SVM to train and predict anomalies. I would like to train the model using cross validation in an easy way as I have done with a multiclass SVM with caret in R. Now, I train the ...
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13 views

Support Vector Regreesion Model/Equation with SMOReg in WEKA?

I used Weka SMOreg with nonlineer kernels (RBF and polykernel), it only gives me back the support vectors and predicted values as an output, but not the actual regression equation that is used to ...
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63 views

High accuracy during cross validation, low accuracy on test set

I'm currently trying to build a tennis prediction model. Unfortunately, I have some issues that I hope you could help me to handle. I have 1110 examples of matches from the year 2013, with their ...
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9 views

How to get best possible model in ONE CLASS SVM

I am using one class SVM on a project. I do not have labeled data. I am not sure what should be the judging criteria to identify the best model. Any Suggestions? Thanks in advance!
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23 views

Are Decision Function and Separating Hyperplane the same?

In many machine learning algorithms such as SVM, GBM, Logistic Regression, etc., are Decision Function and Separating Hyperplane the same?
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54 views

sklearn - overfitting problem

I'm looking for recommendations as to the best way forward for my current machine learning problem The outline of the problem and what I've done is as follows: I have 900+ trials of EEG data, where ...
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37 views

SVM classification

I have a small data set of 450 instances with feature vector of 21 feature and and I need to classify (binary classification) I applied Support Vector Machine Kernel Linear and RBF. In my case RBF ...
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Assigning weights (or importance) based on frequency

Data I have prepared the data (67 dimensions) in the form of vectors like: 1,0,0,0....66 times 0 (just 1st dimension having ...
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imbalanced SVM hyperplane

Does a SVM give a parallel hyperplane to the best one when the data is imbalanced? The issue could be solved just by finding which one of thoses parallel hyperplanes is the best one?
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How to know which Kernel is better?

I am working on an Image recognition software - My first question is since I already explicitly turm my training images to features vector (and also my test images) what is the point of using ...
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Effective Linear Regression for datasets with missing values in explanatory (independent) variables

I have an econometric dataset of countries consisting of features such as GDP, GDP per capita, internet penetration rate, life expectancy, poverty etc. There are a ...
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SVM and concatenation of features

For example I train my SVM on 3 features set of different size: Training data size [rows cols]: features1 [nsamples 100] features2 [nsamples 50] features3 [nsamples 128] And for each feature set ...
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How to calculate decision boundary from support vectors?

I want to obtain decision boundary of SVM using OpenCV 2.4.11, but it seems that it's not returning it explicitly, but only support vectors. How we can calculate decision boundary from support ...
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What is the difference between Support Vector Machines and Conditional Random Fields models in the context of Named Entity Recognition?

Can someone intuitively explain how Support Vector Machines (SVM) and Conditional Random Fields (CRF) models can perform Named Entity Recognition (NER), and the difference between them conceptually? ...
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How to select best cross validated SVM (support vector machine) model when using K fold CV (5)?

How to select best cross validated SVM (support vector machine) model when using K fold CV (5)? Edit I used Kfold =5 and have 5 models. I need to select best predictive model. I have "bias" value for ...
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featurizing images of different sizes

I'm training a non linear svm to do classification on images. I'm featurizing the image by creating 3 features for each pixel, its rgb value. My question is: How should i normalize images of different ...
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43 views

Bug in libsvm? Adding a single new example crushes everything! [closed]

I have a set of unbalanced examples, i.e. there is much more negatives than positives. Hence, I decided to use an SVM with weighting. However, when I want to figure out the 'best parameters' (cost and ...
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63 views

Advantages and disadvantages of machine learning hyperparameter optimizers [closed]

What are the respective advantages/disadvantages of the following optimization algorithms for ML applications? (that is, to optimize the hyperparameters of a SVM, RForest, Boosting model, etc.). In ...
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8 views

Why might an observation's predicted class probabilities differ from its predicted response in a svm classifier? How can I use both?

I'm using support vector machines to classify an image. I'm new to statistical learning. Using the ksvm package in R I've found that the predicted response is often different than the class with the ...
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31 views

Is this training dataset enough for training and testing classification model?

My training dataset contains just 2 classes with 40 features. In case 1, class 1 has 35 samples and class 2 has 700 samples. In case 2, class 1 has 65 samples and class 2 has the same value as ...
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Feature Normalization & Learning

I'm working on a cell classifier (as in Biological Cells) using images obtained by microscope. Right now I have about 12 Features written (color,width-height ratio, shape, couple of texture features, ...
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SVM - running time for detecting if data is linearly separable?

If my understanding is correct, one way to check if a set of $m$ data points is linearly separable is to use support vector machines to find a maximum margin hyperlane for separating the data; the ...
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43 views

How do I use weight vector of SVM and logistic regression for feature importance?

I have trained a SVM and logistic regression classifier on my dataset for binary classification. Both classifier provide a weight vector which is of the size of the number of features. I can use this ...
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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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21 views

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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38 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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55 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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21 views

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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24 views

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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13 views

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 ...