Support Vector Machine refers to "a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis."

learn more… | top users | synonyms (1)

0
votes
0answers
18 views

Support vector regression - data visualization

I have some training set (y1, x1) , (y2, x2), ..., (yn, xn). Output yi is a real number and the features xi live in a real d-dimensional space. I am here in the high dimension case where n = 30 and d ...
0
votes
2answers
45 views

One Class SVM strange decision boundary

I am trying to plot the decision boundary of a One Class SVM. This is a 2 dimensional representation of my training data And here the picture of the prediction obtained on the training data ...
0
votes
0answers
6 views

Linear-time approximation to kernel SVM?

Scaling kernel support vector machines to large datasets is a very challenging problem. For linear SVMs, PEGASOS is able to learn efficiently online, so training time scales linearly with the size of ...
1
vote
0answers
9 views

How do i generate variables that are relevant only for some classes?

I want to generate data for classification. I've generated data with 10 variables with two are relevant for all classes and 8 noise. now, I want to generate variables that are relevant just for some ...
0
votes
1answer
29 views

Selecting most realistic C and g params after gridsearch

I just ran an extended SVC gridsearch in libsvm on about 9000 multi-dimensional vectors representing a time series. Here are the highest scoring results: ...
0
votes
0answers
38 views

Logistic Regression, SVM or NN?

Just attended Andrew Ng’s online course on ML and although I’ve understood the methods I seem to be missing the intuition on where to apply them in terms of classification problems. What are the ...
0
votes
0answers
10 views

SVMlight support vectors inside the margin

I am trying to understand a linear model obtained with SVMlight. The model file contains a number of support vectors and their corresponding $\alpha_i y_i$. Since all $y_i \in \lbrace -1,+1 \rbrace$, ...
0
votes
0answers
18 views

How to retrieve the prediction equation in R?

I have developed a prediction model prototype in R. The model uses Support Vector Regression to predict. But I need to develop the entire solution in Visual C++ for a real life implementation. I ...
0
votes
0answers
11 views

How to choose negative training sample for Classification problem

Choosing positives sample is a relative straightforward task, but I'm having some problem on determine what should I use for the negative example. I'm working on a SVM binary classificator, trying to ...
-1
votes
0answers
13 views

Pattern recognition training base division

1) A dataset (100 samples, 10 classes, 8 chars each) was multiclassed obtaining a good accuracy with an external dataset. 2) The same dataset was divided in half (50 samples, 5 classes, 8 chars each) ...
1
vote
2answers
50 views

Linear SVM prediction time is scaling in an unexpected manner based on training data

I'm using LIBSVM to do some training as it was recommended by Andrew Ng and is used under the hood in SciKit Learn. LIBSVM is doing something different than what I expect though: My beliefs are as ...
0
votes
0answers
18 views

Most efficient vector construction for Dynamic Time Warping

I'm in the process of folding FastDTW into my SVM and the question now is how to best format my data (irrespective of normalization). Here's an example of what I'm attempting to do - given two 3d ...
1
vote
0answers
17 views

Feature boosting via rescaling in logistic regression and linear SVMs

If I were expressing a problem in terms of binary features, all encoded as {0,1}, could I boost some features by encoding them as {0,2}? Would the effect change based on whether I used either of the ...
1
vote
2answers
51 views

How to choose the training, cross-validation, and test set sizes for small sample-size data?

Assume I have a small sample size, e.g. N=100, and two classes. How should I choose the training, cross-validation, and test set sizes for machine learning? I would intuitively pick Training set ...
0
votes
2answers
28 views

Instance weighing in libsvm/liblinear

I often use the instance weights with Libsvm for classification problems. http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances Does anyone know the details of the algorithm that ...
0
votes
1answer
35 views

How to format multi-row time series data for LIBSVM regression

I would have expected this to be covered in detail by the LIBSVM tutorial but after hours of wasting time googling for answers I've had to throw in the towel. What I am trying to do is rather trivial ...
1
vote
0answers
35 views

SVM versus Bayesian regression example(s)?

I am trying to track down examples where some basic problems have been tackled via both classical Machine Learning algorithms and more formal statistical methods. In particular, I'm interested in ...
0
votes
1answer
33 views

Handle large set of features using SVM

I have a biological dataset with 30.000 features (genes) and 1000 data points (cells). Basically I have two major classes of cells: 1 and 0 with a distribution of 90/10. Now I am trying to classify ...
-1
votes
0answers
44 views

Sales Forecasting using Support Vector Machine

I have sales data for last three years 2011-2013. I want to use Support Vector Machine technique in R to do the predictions. I just wanted to know that the approach that I am using is correct or not? ...
0
votes
0answers
6 views

libsvm with diffrent count of Keypoints [migrated]

I would like to use libsvm for a keypoint detection algorithm. Each keypoint has 36 features, but each sample of an Object has a diffrent count of keypoints... Is it even possible to train with ...
1
vote
0answers
17 views

How do you deal with different distance based features?

If I have a model where the set of features where a cosign distance measure makes sense for some of the features, and a Euclidean distance measure makes sense for the others for example using a BOW ...
0
votes
0answers
21 views

Examine SVM result by plotting histogram of decision values of training samples

I'm working for object detection(computer vision) and have some problems in SVM training. My training configuration is as below. Balanced training set (positive 3998/ negative 3998) The dimension of ...
0
votes
0answers
19 views

How should I do a grid search for the gamma in SVM?

I know that you can do a grid search for the C parameter (-c) in libSVM by going through value that go from 10^-5 to 10^5. How should I go about finding the optimal epsilon parameter (-p) ? Is ...
0
votes
0answers
21 views

Choosing fold size for highly Imbalanced dataset + nested CV + svm

I am trying to classify a dataset with ~1000 points. 90/10 is the class ratio - super imbalanced. Here are the following steps I did: Use 20 relevant features from previous knowledge Remove highly ...
0
votes
0answers
10 views

feature weights in structured support vector machine

I like to find the feature weights in a structured SVM for ranking the features w.r.t. importance. I know that in a binary SVM the weight vector can be written as a linear combination of examples. But ...
0
votes
0answers
4 views

what is the meaning of the Samples in NER?

I would like to know in NER (Named Entity Recognition ) problem , which concept should be considered as samples? each token as a sample? or each sentence ? or each Named Entity should be considered ...
1
vote
0answers
46 views

Adaptive Boosting vs. SVM

I am working on a binary classification case and comparing the performance of different classifiers.Testing the performance of adaboost algorithm (with decision ...
0
votes
0answers
32 views

Find linear SVM feature weights using libsvm

I'm trying to use linear SVM to do some feature selection. I'm using libsvm, but I cannot figure out how to find feature weights. The model file created looks something like this: ...
0
votes
0answers
27 views

How calculate average probabilities in MLP or SVM?

I have a system that find best model (best inputs and parameters of MLP/SVM) model in a financial problem for every inserted database and create a specific model for a specific data sample. I'm using ...
0
votes
1answer
62 views

Feeding a layer from a deep-learnt neural network into an SVM

In http://jmlr.org/proceedings/papers/v32/donahue14.pdf, it is stated: Our top-performing method (based on validation accuracy) trains a linear SVM on DeCAF6 Can you delineate in a way ...
1
vote
0answers
37 views

How to give an input when you are using Machine Learning method in R

I am new to R and machine learning algorithms. I have basic knowledge of different machine learning algorithms. I have four years of daily sales data.I am trying to predict sales using Support Vector ...
0
votes
0answers
36 views

Parameter optimization of SVM

Currently I am using SVM to perform some classification task. I use libSVM with Matlab interface. From the practical guide of SVM (Link), we know that there are two parameters need to be tuned, namely ...
1
vote
0answers
11 views

Polynomial Kernel

Consider the polynomial kernel: $$K(\boldsymbol{x}, \boldsymbol{x}') = (\boldsymbol{x}^{T} \boldsymbol{x}'+c)^{d}$$ What exactly is the role of $c$? If $c$ is large, does this indicate that lower ...
0
votes
0answers
32 views

Prediction using Support Vector (SV) method in R

I came to know that using SVM method we can predict the future value more accurately than other normal methods (like ARIMA). My question is how do we give the future index value (let's say 101 when we ...
2
votes
2answers
35 views

Regression vs Multiclassification

I was working with SVR, and wondering, why can't I solve a natural regression problem as a multiclassification task ? Example: I have for a regression problem: targets 1, 5 and 10, trying to fit ...
2
votes
1answer
35 views

Is there some theory of SVMs with infinitely many data?

I am trying to understand what does it means to have a (linear) SVM classifier (with soft margins) given the generative model of the data. And I realize I have not seen any paper on it, nor can I ...
0
votes
0answers
35 views

Estimate SVM a posteriori probabilities with platt's method does not always work

I have a problem.. I'm trying to create a multiclass SVM with probability output. The SVM is working so far, what means, that the accuracy is ok (see the last picture). But the probability estimation ...
0
votes
1answer
23 views

SVMs and solution

In SVMs, is the solution to the minimization problem $$\textbf{w} = \sum_{i=1}^{n} \alpha_i x_i y_i $$ and once we know $\textbf{w}$ we can get $\textbf{b}$? In plain English can somebody please ...
0
votes
1answer
25 views

Duplicate data for SVM

Can we use duplicate data as an input to SVM? The duplicate data that I mean is, let say we have 50 of same data (maybe being duplicate) from total of 100 data. Will this kind of data effect the ...
1
vote
1answer
70 views

Support Vector Machine Question

I need help with the following problem. I provided my current (partial) solution, and I hope someone can correct me and/or give me suggestions as to how I should solve the parts that I've left out. ...
0
votes
0answers
17 views

Dummy variables in logistic regression vs. svms

Suppose $y$ is a binary outcome variable and $x$ is a categorical predictor variable that takes three levels (1,2,3). In this case, you would create two dummy variables $x_2, x_3$. So $x_2=1$ if $x=2$ ...
3
votes
1answer
84 views

SVM in R package e1071

I am trying to use SVM to make a prediction (True or False) on a dataset with many independent variables. I am wondering how I can identify the most useful variable in making the prediction. I ...
0
votes
0answers
17 views

Joint label between two datasets produces significantly worse results

I have two sets of corresponding example labels with more or less the same features. Let's call first one label A and the second one label B. Both labels are binary. The classification accuracy of ...
0
votes
0answers
19 views

Can we use log-likelihood to cluster classes?

I have an SVM classifier for m classes and n data points (somewhat evenly distributed across each class). Could I use the resulting MxN log likelihood matrix to merge classes that are similar?
0
votes
0answers
31 views

Entire data considered as support vector

I am currently learning to use support vector machine as classification. I have a data set with 161 observation and 18 dimension. I get 160 support vectors using svm function form R package, e1071. ...
0
votes
1answer
48 views

e1071 svm queries regarding plot and tune

I am new to R and I am learning the e1071 packages' svm function. Following are the few questions I have. How does the plot function work? I cannot understand the plotting case with more than 2 ...
0
votes
0answers
10 views

optimisation procedures before training SVM

I'm using the LIBSVM in Java for classification with 200 documents in inputs. I build/train the SVM using the same input training data. My response time for preprocessing of documents (tokenization, ...
0
votes
1answer
28 views

Feature Selection using (low) MCC

I have approximately 1200 input parameters that I am trying to whittle down with the following rough process: 1) Fit rbf SVM with n = 1200 parameters and calculate Matthews Correlation ...
0
votes
1answer
14 views

Differences in SVM performance

Why would a polynomial SVM have better performance than a linear SVM but the same performance as a radial SVM?
0
votes
2answers
16 views

SVM and kernels

Suppose you are given a binary classification problem. How do you know that you have to map the problem into a higher dimensional space? In other words, how would you know that a linear SVM is not ...