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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two margin comparison and one conclusion?

I read following notes, and couldn't get it. any idea or hint would highly appreciated. a SVM classifier using a second order polynomial kernel. The first polynomial kernel maps each input data x to ...
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1answer
26 views

categorical data normalization in SVM Classification [on hold]

I have a set of features (contineous + categorical)...I have converted the different categories to numerical, for example (object1, object2, object3) = (1,2,3)..etc. and ran SVM... I obtain high SVM ...
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171 views
+50

Compute the probability that the provided classifier label is correct

A binary SVM classifier provides a label $y_c^{(i)}$ for each $i$-th sample provided. This is not assured to be corresponding to its true label $y^{(i)}$, since the classifier could have computed a ...
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2answers
32 views

Semi-supervised Learning Training

I have got some data partially labelled. Therefore, I would like to apply semi-supervised learning for this dataset. Basically, I trained the Support Vector Machine (SVM) using the data with labels ...
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5 views

Semi supervised classification on multivariate data for outlier detection

I have a large multi dimensional unlabelled dataset of cars (price, mileage, horsepower, ...) for which I want to find outliers. I decided to use the sklearn OneClassSVM to build a decision boundary ...
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13 views

Forward sequential feature selection improving classifier performance?

I was in a bit of a conversation with a co-worker about using forward selection. My training data is on order of ~6,000 w/ dimensionality of 1,200, and testing data on order of ~3,000. Currently, I'm ...
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1answer
33 views

Kernel methods in machine learning?

I am beginning to tackle geostatistics problems where I tried to apply kriging(gaussian processes) to interpolate demographical water drop. According to my understanding, kernel methods are something ...
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1answer
57 views

How to use the kernel trick on data that can't be visualized

I'm reviewing the kernel trick and there are a lot of toy examples of how a 2D classification which can't usually be separated by a linear SVM can be separated in 3 space. This is fine, but how is ...
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28 views

Support Vector Machine image classification in R

I'm looking for some direction for creating/running a support vector machine (SVM) classification on a multi-band Landsat image in R. What I have: Landsat 8 image with 8 bands plus a NDVI, and ...
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2answers
56 views

Why do one-versus-all multi class SVMs need to be calibrated?

On the wiki page for multi-class support vector machines (https://en.wikipedia.org/wiki/Support_vector_machine#Multiclass_SVM) it states that "it is important that the output functions be calibrated ...
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21 views

How do I learn algorithmic trading? [closed]

How do I learn how to set up an algorithmic trading system? I have taken Andrew Ng's Machine Learning course and am completing data sciences specialization on Coursera, am familiar with R and Octave. ...
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1answer
39 views

Why does hypothesis of SVM output 0 or 1?

Prof. Andrew Ng in his Machine Learning class says that unlike Logistic Regression, SVM outputs hypothesis as 1 or 0. But I don’t understand why SVM's behavior differs from that of logistic regression ...
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14 views

What is the time complexity of binary classification of SVM?

One of the earliest solution to the SVM problem is SMO applied to dual form.What is the time complexity of SMO algorithm? What is the best known time complexity to solve SVM algorithm (non linear)?
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13 views

SVM on 1 dimensional data

I have a data vector which I have annotated. But it only includes one feature, and data is generally on dimensional. Is there any way to apply SVM on 1-dimensional data?
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3answers
35 views

Comparing SVM Model

Pardon my understanding of SVMs as it is very little. We often hear of ensemble classifiers and stuff like this. Say if i were to have 3 different SVM Models for the same dataset predicting a ...
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1answer
29 views

SVM Three Way Classification

I would like to verify the following methodology for using SVMs for three way classification. That is, the response $Y$ can be either $\{-1, 0, 1\}$: First train an SVM to distinguish between ...
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52 views
+100

Learning user behavior that changes over time

I am learning a model using SVM that will predict user behavior of some kind. Simplifying this model, each example in the feature space contains some features: $f_1,f_2,..,f_n$ and a class $a$ that ...
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26 views

Parameter selection in one class svm

Is there any accepted way to select the best parameters for one-class svm with a linear kernel in r? I have managed to run the svm function in e1071 but it is super sensitive to the model ...
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1answer
45 views

SVM in R (package e1071): predicting class using predict()

I have difficulties to understand predict.svm. Please find an illustration of my confusion below. As we can see, results are different depending on the ...
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2answers
29 views

How to include a pattern for'unknown' for an SVM classifier?

I am doing a classification of heart beat with SVM. There are five kinds of beats in my training data. I plan to add a new kind of data named 'unknown' beat. If there is no unknown beat, one ...
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5 views

Data sets with labels voting, label confidence or three values labeling to learn a model on noisy or ambiguous labels

I'm working on my first algorithm and I don't really know where to look for the right data set to use to generate my models. I need a data set where for a binary class, I can label some of the points ...
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2answers
22 views

Data normalization for RBF kernel

I have a matrix of values where rows are individuals and columns are attributes. I want to extract a similarity value for every pair of individuals, and I use an rbf kernel: $$k(x_i,x_j) = ...
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14 views

Query re. how to set up an SVM, which SVM variation … and how to define a metric

I’d like to learn how best set up a Support Vector Machine for my particular problem (or if indeed there is a more appropriate algorithm). My goal is to receive a weighting of how well an input set ...
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12 views

Good implementation of SVM with operator valued kernel

I've already come across the question Vector Valued SVM but the replies doesn't point to SVM with any Operator Valued Kernel. I understand that struct svm can solve the same by solving inference ...
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1answer
94 views

The difference between logistic regression and support vector machines?

I know that logistic regression finds a hyperplane that separates the training samples. I also know that Support vector machines finds the hyperplane with the maximum margin. My question: is the ...
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22 views

Learning Curve Meaning

I have made a learning curve that looks like this: Why wouldn't it be more like both training and cross-validation score begin low and both gradually increase with more samples? Why does one start ...
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10 views

SciKit Learn - Catch local and trend properties in data?

I have a time dependent dataset with 10 independent and 1 dependent variable. I am trying to fit a multiple regression to make continues predictions. Currently i converted the timestamp into multiple ...
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34 views

Which method to use for load forecasting

I have smart meter data set that has consumption readings collected over a year and a half for every 30 mins. What I am trying to do is short term load forecasting. The data set has just three columns ...
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Measure performance of a multiples classifier that is a combination of binary support vector machines

I am comparing the performance of multiple schemes to combine binary SVMs into one multiples classifier. I was hoping to use the cross entropy, however I cannot figure out how to apply it. For ...
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49 views

Practical exercise to solve SVM and logistic regression

I have a simple task regarding SVM and logistic regression, even though I know the theory behind SVM and logistic regression I still have a problem to solve them. Exercise. We want to learn a ...
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16 views

SVM + CRF for classifcation

I am thinking about using the output of an SVM classifier, i.e. the distance of the samples from the decision boundary, as unary terms of a CRF. The feature space will be different, furthermore the ...
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1answer
45 views

One vs all Linear SVM Cross validation -Parameter selection

I'm performing one vs all classification (SVM) for a dataset. Since I'm using a linear SVM the parameters I need to tune and select are-Tolerance and C. I'm a bit confused on how to go about doing ...
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20 views

Can I use the ECDF of a ~NB variable for SVM?

I am hoping to use SVM or NN for a large dataset (N ~ 4-8 million). Some of the features are counts, which have a distribution that is approximated by the Negative Binomial in the bioinformatics ...
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21 views

Sensitivity analysis of machine learning techniques

As you know we can have sensitivity analysis (sensitivity of output(s) based on changing of inputs) in different kinds of regression. Can we have sensitivity analysis for machine learning techniques ...
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why boosting method is sensitive to outliers

I found many articles showing that boosting methods are sensitive to outliers, but no article explains why. In my experience, I feel outliers data is bad for any machine learning algorithms, but why ...
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1answer
39 views

Can we boosting or stacking with different input variables for each model in machine learning?

I have a question about Boosting and stacking in machine learning. Suppose that I will train neural network, SVM and logistic regression using optimization algorithm to optimize best inputs in first ...
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How do I evaluate the accuracy in estimating a set?

Say I have a training set $D$ of only positive examples, from which I learn a set $A$ such that $D \subseteq A$, and $A$ needs to be the "full" set -- i.e. I try from a small set generalize to a ...
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2answers
92 views

How to transform categorical variable into numerical variable when using SVM or Neural Network

To use SVM or Neural Network it needs to transform categorical variables into numeric variables, the normal method in this case is to use 0-1 binary values with the k-th categorical value transformed ...
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1answer
50 views

Support Vector Machines and the curse of dimensionality

I am reading this paper: "Automated MR image classification in temporal lobe epilepsy", by Focke et al. NeuroImage, 2012. The authors use support vector machines to classify subjects between healthy ...
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18 views

Does centering or mean normalizaiton alone every help in feature scaling?

In feature scaling, one way is to subtract the mean (centering) and then divide by the standard deviation for all data points. Suppose we just centered the data and didn't divide by the standard ...
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1answer
29 views

Correct arguments for svm() function in R

I'm looking to implement a linear and non-linear SVM in R but having some confusion over which argument to use in svm(). For the linear SVM I want to add in the penalty $\gamma$ for soft margin. This ...
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16 views

Multivariate Non-parametric Regression

What is the most well known(or most effective) method for multivariate nonparametric regression? I am surprised that there is no 'popular' support vector machines' based method.
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38 views

Mean Average Precision in Matlab with liblinear and vlfeat

I want to find the mean average precision (meanAP) from a classification problem. I am using liblinear for classification and I am trying to use vlfeat for the precision because it already includes a ...
3
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1answer
265 views

Why doesn't a non-linear kernel improve accuracy in high dimensions compared to a linear kernel?

I read somewhere that if the number of dimensions in your feature set is very high, then a non-linear kernel such as RBF (or any other) may not help in increasing accuracy compared to a linear kernel. ...
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40 views

Tune function in R for SVM tune.svm() scale data?

I have been trying to use SVMs for a while under R but I have very big troubles to find informations about those functions. For instance, you can read that the svm() function from the e1071 does ...
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1answer
22 views

Which one to choose and when? One-v/s-One and One-v/s-All classification for multi-class classification

In case of multi class classification task, how do we decide which among the two options viz. one-v/s-all and one-v/s-one do we choose for model building? Is there some criterion based on which we ...
0
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1answer
26 views

Cross-validation ($3$-fold) for optimizing ($C$, $\gamma$) in RBF-SVM

Let $\mathcal{X}$ be a training set which will feed a binary SVM with RBF kernel. $\mathcal{X}$ consists of $10$ positive examples and $100$ negative examples. I am interested in optimizing the ...
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1answer
103 views

Time series forecasting using Support Vector Machines

I have been trying to use Support Vector Machine method for time series forecasting. I have seen allot of research papers, but nobody shared the code or tool they have used for that. Got some ...
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9 views

ensemble model for SVM

I did a nested 5-cv and the resulting models are unstable (high variance among the hyper parameters C and gamma of SVM). So, I don't know how to choose C and gamma for the "final" model. I read that, ...
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35 views

Why does Support Vector Regression slow down after several iterations?

TLDR: Why does SVR slow down on my machine after multiple runs in IPython/sklearn? I'm trying to grid-search to optimize some parameters in Support Vector Regression with a problem that has 2 ...