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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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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16 views

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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5 views

svm audio classification [on hold]

I am working in project for classifing a human voice with SVM and it is based on the MFCC coefficient. I have the program in matlab to calculate MFCC, it gives 12 vector of MFCC. and I have the ...
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7 views

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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21 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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2answers
44 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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8 views

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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9 views

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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17 views

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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1answer
21 views

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 ...
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7 views

Feature Matrix vs Feature Vector

In my application I have essentially $n$ areas of interest in an image. The image is circular in nature so the zones are slices. Each zone has 7 features associated to it. The goal is to detect ...
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26 views

How to find the correct picture for this SVM classifier?

There are different SVMs with different shapes/patterns of decision boundaries. The training data is labeled as yi āˆˆ {āˆ’1, 1}, represented as the shape of circles and squares respectively. Support ...
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1answer
53 views

Non-probabilistic vs probabilistic frameworks for decision theory in metric spaces

I have a task to make a decision, say to classify an object as $X$ or $ \overline X$. However, $\overline X$ usually means everything else and you only have positive examples of $X$ and not so many ...
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8 views

SVM multiclass experiment design

I know there have been a few questions regarding this topic but I wanted to see if this was a valid design as there was no clear answer that points to such. I have a dataset with multiple classes ...
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9 views

Constraint on maximal margin classifier

When I learned the maximal margin classifier, I saw the following definitions: \begin{align} &{\rm maximize}_{\beta_0...\beta_p}M \tag{1} \\ &\sum_{j=1}^{p}{\beta_j}^2 = 1 \tag{2} \\ ...
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27 views

Advantages of LS-SVM over SVM

My teacher asked me to do a research on LS-SVM, I know what is LS-SVM and how its mathematically different from SVM. I have found lots of papers that shows that for large-scale problems LS-SVM have ...
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22 views

What does the number 'Kernel Option' refer to in SVM?

I read that the performance of some kernel functions in SVM can change if we change the number known as kernel option. For example, this article states that kernel option of value 2 was used, ...
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23 views

Why is my SVM multiclass classifier only correctly predicting a few classes?

I'm doing an online course to learn the basics of Machine Learning. This exercise is on how to use a SVM classifier with multiple classes. While the problem is specific to question 2 from this ...
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58 views

Linear SVM feature weights interpretation. Binary classification, only positive feature values

I'm using clf = svm.SVC(kernel='linear') on a data set with only two classes $y \in \{-1, +1\}$ and the feature values of all samples are normalized between 0 and ...
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11 views

Getting significantly different results for a classification task when using two similar approaches

I am new to machine learning and I classify abstracts of scientific papers retrieved from two different disciplines. I use RTextTools package and I apply two different approaches for the ...
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9 views

Link between the FDA and LS-SVM

I am reading tutorial written by Johan Suykens:Least Squares Support Vector Machines On page 19,he mentions link with kernel Fisher Discriminant Analysis Project ...
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1answer
13 views

Normalize all data before cross-validation or normalize every train part separately and use same properties for test part?

Suppose that we want use 5-fold cross-validation for a support vector regression(SVR) model. We should normalize total data before cross-validation process or we need normalize every train part ...
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1answer
26 views

Too many bagging estimators?

I am bagging 20 SVMs using the full training set. I have found the best SVM params using grid search. The validation performance is quite good, but performance on the training set is disappointing. ...
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1answer
43 views

What's a better classifier for simple A-Z letter OCR: SVMs or kNN?

Disclaimer: I'm nearing the end introductory machine course so knowledge on the subject is not too strong (yet)! Context: I'm thinking of building an optical word search solver for a term project ...
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27 views

How to output as unclassified object in svm multiclass classifier?

I am developing an image classifier using opencv,python.I am using svm from opencv. The image classifier classifies Animals,vehicles and Humans.It works fine.But when i give the image of 'nature ...
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33 views

Assigning weights to a multilabel SVM to balance classes

How is this done? I am using Sklearn to train an SVM. My classes are unbalanced. Note that my problem is multiclass, multilabel so I am using OneVsRestClassifier: ...
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17 views

Support Vector Machines: On turning the maximization of the margin into a minimization problem

I am currently reading on the mathematics underlying the SVM algorithm where one wants to maximize the margin given by $\rho = \frac{2}{\sqrt{<\mathbf{w},\mathbf{w}>}}$ where ...
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12 views

How to assess text classification results when extreme sparsity is present?

I try to classify documents based on bag-of-words single word approach. I employ R with its RTextTools to use SVM. My text files are like below: TRAIN ...
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1answer
28 views

Use cross-validation trained models for out-of-sample prediction or train a new model using whole data?

I'm using support vector regression(SVR) model and 5-fold cross validation for price prediction. which one is more appropriate after training models for ...
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3answers
150 views

What is the difference between Machine Learning and Deep Learning?

OK, I know there is a lot of topic regarding this in the internet, and trust me, I've googled it. But things are getting more and more confused for me. From my understanding, Deep Learning (DL) is ...
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2answers
33 views

Comparing a linear classifier and SVM?

On what metrics can we compare the classification performance of a linear classifier and an SVM to highlight the advantages of each other and note under what circumstances one performs better than the ...
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1answer
30 views

Accuracy on the test set do not change. Why?

I train a SVM classifier using 36 features. If I use all the features, the train accuracy is about 0.96, the test accuracy is about 0.77. Then I change the number of features. The train accuracy drops ...
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1answer
12 views

e1071: CVE returns linear separation, Hold-out returns large error

I'm using the Adult dataset that can be found here: http://archive.ics.uci.edu/ml/datasets/Adult After taking a sample of the dataset, I use the svm function of e1071 to obtain the accuracy with a ...
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8 views

a question on formulation the svm optimization problem

I was following andrew ng machine learning course. I didn't understand something in the part where he was trying to formulate the optimization problem for svm. Specifically, How can you get ...
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22 views

Normalize data by features or by instance?

Before applying Kernel tricks in SVM, I know I should normalize data at first. (1) But should I normalize data by features or by instances? For example, if I have n data points, each data point has d ...
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1answer
59 views

100% training accuracy despite a low cv score

I am working on an assignment where we have to study the affect of gamma and C parameters on SVM with RBF kernel. I use python's sklearn library and grid search with 10 fold cross validation (with a ...
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Extract a sub-distribution with specific characteristics

Imagine that I have a distribution of some data. For example a distribution of natural numbers [1, 10, 45, 89, 12, 9, 4, 100]. I characterize such distribution with 2 features: the mean = 33.75 and ...
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1answer
30 views

SVM: Does C increase variance or stability (bias)?

I was learning about SVM using 2 sources: Andrew Ng Machine Learning course from Coursera and Stanford 'Statistical Learning' (from Trevor Hastie and Robert Tibshirani). And I encountered the ...
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40 views

hyperplane in svm

What is the correlation between finding hyperplane and use it in prediction process of svm ? I still don't get it, after finding hyperplane, then what ? how it helps to find correct class from test ...
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Multiclass classification one versus one with ensemble

If I use an ensemble, which consists of four classifiers, in order to classify my data into three classes. Further, suppose I use the one versus one strategy. My question is: How to fuse the ...
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60 views

Any methods to reduce the execution time taken by support vector regression libsvm algorithm?

I am currently working on dataset with 4 input dimensions and 1 output, in each approximately 7000 values hence overall 7000X5 dataset. I chose to use support vector regression and this takes a long ...
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1answer
54 views

High Recall - Low Precision for unbalanced dataset

Iā€™m currently encountering some problems analysing a tweet dataset with support vector machines. The problem is that I have an unbalanced binary class training set (5:2); which is expected to be ...
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24 views

How can I format my list to give it as input to svm.train() in opencv3.0 using python

I am using opencv3.0,My IDE is pycharm I have two lists one list of training_set and one list of trainig_labels. training_set is a list of lists like ...
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43 views

How to use sklearn Pipeline with custom Features? [closed]

I am doing text classification using Python and sklearn. I have some custom Features which I use in addition to vectorizers. I would like to know whether it is possible to use them with sklearn ...
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22 views

Multiclass SVM result [closed]

I have a problem with multiclass SVM result. My classes are {-1,0,1} and my CVed precision is {0.2,0.8,0.8}, where contribute with -1 poor classification. If I change classnames to {6,5,4} my CVed ...
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26 views

Are variables influential in random forest also influential in svm?

This paper https://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf highlights the following approach to feature selection: ...
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2answers
77 views

“Good” classifier destroyed my Precision-Recall curve. What happened?

I'm working with imbalanced data, where there are about 40 class=0 cases for every class=1. I can reasonably discriminate between the classes using individual features, and training a naive Bayes and ...
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21 views

Showing a bound on the $L_2$ error in the N-sparse approximation of a vector

This is a supposedly 'trivial bound' from Donoho's Compressed Sensing paper - trying to figure out where it comes from. Assume that $\theta$ is a vector that obeys the following constraint for some ...
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8 views

Is it possible to predict probabilities with Stochastic Gradient Descent with “hinge” loss?

I want to use SVMs to make predictions on a large dataset. I am using Stochastic gradient descent (Python SGDClassifier) with hinge loss. My problem is that I want to predict probabilities but the ...