Questions tagged [svm]

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

Filter by
Sorted by
Tagged with
0
votes
1answer
419 views

How manage missing value with SVM prediction

How can we manage missing value when we train a SVM Model ? In particulary, if the SVM model is already train, how can we predict the class of a new observation if there is missing value ? Thanks
0
votes
0answers
8 views

Apply panel data techniques to a panel where observation can be assumed to be independent?

I'm working with a data-set and am unsure if I should apply specific panel data techniques to it or not. The data consists of panel data for municipalities in the Philippines and damage cause to rice ...
0
votes
0answers
24 views

Pros and cons of different MKL algorithms

I have been using multiple kernel learning (MKL) to train a classifier and got some exposure to the field. However, I am quite new to machine learning and I have only an intuitive understanding of the ...
0
votes
0answers
18 views

k-fold cross validation and support vector machine

I have a dataset of 1877 rows and 6 independent variables and 1 dependent variable so the dimension of the dataset is 1877 x 7. Using the tune(svm,...) function I searched for the best gamma and cost ...
0
votes
1answer
15 views

Speech recognition (SVM) different signal lengths

I am developing a small project on speech recognition, the idea is to classify sound sources by Support Vector Machines. My dataset consists on 45 signals, however, they all have different lengths, ...
10
votes
1answer
5k views

Single layer NeuralNetwork with ReLU activation equal to SVM?

Suppose I have a simple single layer neural network, with n inputs and a single output (binary classification task). If I set the activation function in the output node as a sigmoid function- then the ...
2
votes
1answer
25 views

Logistic PCA and the train/test split

I did a lot of readings about how to do PCA with train/test split. see PCA and the train/test split I understand that we should apply the PCA on train set and then apply the same transformation to the ...
0
votes
1answer
28 views

SVR optimal hyperparameters are Epsilon = 0, Cost = inf?

I'm running an rbf-kernel SVR with GridSearchCV. I'm optimizing epsilon, cost and gamma. In my hyperparameter gridsearch, the optimal parameters appear "unbounded". Specifically, any epsilon under 1 ...
0
votes
0answers
24 views

Increase in SVM Classifier performance after binning

I've been working on a classification problem, and I ran into something rather strange. The original problem has continuous features and three labels. I then mapped the continuous features to binary ...
1
vote
1answer
168 views

What are w and b parameters in SVM?

I've read almost every article on the web, every question regarding SVM here, but I still don't get how to calculate w and b, how did they appear in formula, what is weight and what is bias: $$\vec{w}...
0
votes
0answers
20 views

Is there a concrete proof showing that hinge loss is an upper bound on the 0-1 loss? [closed]

While it is stated in several places that the Hinge loss is a convex upper bound on the 0-1 loss, is there a proof behind it? From what I have seen, most resources just show the plots of hinge loss ...
0
votes
0answers
18 views

Can I make SVM ignore a feature by making all instances have the same value?

For Binary classification using SVM, I was wondering how to make SVM ignore a feature (e.g. the coefficient of that feature in the decision function become 0)? Will SVM automatically ignore ...
11
votes
3answers
1k views

Why can't scikit-learn SVM solve two concentric circles?

Consider the following dataset (code for generating it is at the bottom of the post): Running the following code: ...
0
votes
1answer
22 views

What is meant by “number of support vectors” in the SVM implementation of scikit-learn

I noticed that decreasing the C regularization parameter tends to increase n_support_ in the solution provided by ...
0
votes
1answer
694 views

How does the shape of a decision boundary in relate between the original and kernel feature space?

I'm trying to get my head around the mathematics and implementation of SVM and hopefully gain some intuition into how kernels work and perhaps being able to, with a bit more confidence, define my own ...
1
vote
0answers
20 views

How to apply svm to a dataset with a numerical (not categorical) dependent variable?

Context: I have some band values from a sentinel - 2 derived .tiff I now want to make a prediction regarding areas where i have no actual field data i.e. make a carbon map of sorts Libraries are: <...
3
votes
1answer
163 views

nonseparable case of classification problem (SVM)

I am learning Soft Margin Classification (SVM) right now. In cases when the classes are non-separable by the usual hyperplane with a margin $M >0$, we modify the constraints and say that it is ok ...
1
vote
0answers
15 views

Are there quantitative methods for determining sample size for SVM?

Are there any quantitative and justifiable methods to help choose minimum sample size for having a 'good' model from SVM? Logistic regression affords power analysis which provides minimal $n$ to ...
0
votes
1answer
46 views

Regularisation with SVM

I am working on a dataset in which I have thousands of binary features and a binary response. From the interpretation side of things I would like to fit a SVM model combined with some sort of ...
3
votes
0answers
34 views

Does distance from the decision boundary suggest higher confidence that the class prediction is correct using SVM?

Does further distance from the decision boundary threshold suggest higher confidence that the class prediction is correct when using SVM with probability estimates enabled? This is not a question ...
0
votes
0answers
15 views

When performing a svm hyper-parameter search for epsilon coef0 and degree which min max and increment values are suggested?

I am using libsvm, but I think this applies to any ML algorithm where these kernels are used. The default implementation of libsvm suggests values for the linear kernel or RBF kernel hyper-parameters ...
0
votes
1answer
23 views

Finding optimal kernel parameters

I want to perform multiple kernel learning on my dataset and apply each (rbf) kernel to a different subset of features to then combine them. I do not want to have the same kernel with a range of ...
1
vote
1answer
34 views

Binary classification: does it make a difference to use zero or minus one as class label?

I'd like to build a binary classification model and I recall reading somewhere that the choice of the labels could have an impact depending on the algorithm. So the two modeling approaches are to ...
0
votes
1answer
346 views

How to increase a particular terms's weightage?

I am doing Text classification using LibSVM in Rapid Miner. I am using TFIDF values for processing documents. I need to Increase weightage of some terms in the documents(for eg. words in BOLD and ...
1
vote
1answer
140 views

Why is it that Support Vector Regression isn't as popular as SVM?

I know that SVM and SVR have different purposes in that the former is used for classification while the latter is used for regression, despite the similarity in the concepts between the two. However, ...
0
votes
0answers
47 views

How does SVM decide the weight/coefficient of features?

I was wondering if someone can kindly provide me with some insights regarding how SVM (binary classification) decides the weights for the features. Say, I have a feature $f_1$ that appears in both ...
0
votes
0answers
13 views

Possible?: Split pdf file on pages where object detection algorithm finds custom object [migrated]

I have scanned documents in large, unseparated pdf files containing many documents. Each document begins with a exhibit number sticker, much like this one: example. The files are scanned in greyscale. ...
1
vote
0answers
24 views

huge difference between RMSE and MAE in non-linear regressors

I am building non-linear regression models such as a Random Forest regressor , a KNN regressor or a SVM using a RBF kernel and I decided to use both RMSE and MAE as evaluation metrics. I know that a ...
0
votes
0answers
13 views

What are some ways to improve the accuracy in this SVM?

I am an absolute beginner in the field of Machine Learning. I am trying to teach a binary classifier using the following dataset https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients ...
1
vote
2answers
42 views

Why is it called $\chi^2$ distance / kernel?

The $\chi^2$ distance function is defined as $$ \chi(u,v) = \sum_{i=1}^n \frac{(u_i-v_i)^2}{u_i+v_i} $$ and the $\chi^2$ kernel function, used in support vector machines, is $$ K(u,v) = \exp(-c \...
5
votes
1answer
3k views

How probabilities are calculated for SVM model?

I would like to know, how probabilities are calculated in support vector machine. I have used Iris data set and here is my decision values for three "SupportVectorMachine" (please find the PMML below ...
15
votes
3answers
24k views

What is one class SVM and how does it work?

I was using one class SVM, implemented in scikit-learn, for my research work. But I have no good understanding of this. Can anyone please give a simple, good explanation of one class SVM?
5
votes
1answer
11k views

Support Vector Machine - Calculate w by hand

I am working on a trivial example of SVM to gain some intuition behind the way it works. In the case of 6 data points, would it be possible to calculate the value of $w$ and $b$ by hand ? Intuitively ...
1
vote
1answer
126 views

How can I standarize/normalize my categorical, factorized features in outliers detection problem?

I'm working on anomaly detection in CTU-13 dataset. Records are labeled and there are a few categorical features with many categories (for example one of the features "State" has over 250 possible, ...
1
vote
2answers
37 views

Is there any possibility of overfitting even after higher AUC, specificity and sensitivity obtained through repeated k-fold cross-validation?

I have built a model where a 10-fold cross-validation was performed 10 times. The average AUC, MCC, specificity, sensitivity of 10 times were reported as the prediction performance. Yet some people ...
0
votes
0answers
8 views

PCA Before Random Forest [duplicate]

I am starting to work in data science and machine learning and I have a little question. I am trying to construct a model that predict a continuous variable, it means, a regression model. I have nine ...
0
votes
1answer
147 views

Normalization for pattern classification?

I'm working off my first independent project for some pattern classification. I'm utilizing some datasets from UCI machine learning, but am not sure on how to start with data normalization. The data ...
2
votes
2answers
102 views

PCA shows overlapping boundaries, then why SVM performs best

I am trying to understand which model might work for a given problem before trying the models, I find this case against my knowledge. Please guide what I am missing. I am new to Data Science. Here is ...
2
votes
2answers
27 views

How to solve MNP (minimum norm) problem in SVM?

I'm reading an article, which says that MNP (minimum norm problem) can be solved as SVM. In the minimum norm problem, we're given a set of points in $R^d$ and need to find a point in convex hull of ...
4
votes
3answers
7k views

SVM - why quadratic programming problem?

In Support Vector Machine, why is it a quadratic programming problem instead of a linear programming problem to obtain the optimal separating hyperplane. I only find, in book references, that the ...
0
votes
0answers
22 views

SVM and Monte Carlo simulation to compute misclassification error rate

I am trying to solve the following problem with R: use simulation to evaluate (by Monte Carlo) the expected misclassification error rate given a particular generating model. Let yi be equally divided ...
56
votes
4answers
36k views

Why bother with the dual problem when fitting SVM?

Given the data points $x_1, \ldots, x_n \in \mathbb{R}^d$ and labels $y_1, \ldots, y_n \in \left \{-1, 1 \right\}$, the hard margin SVM primal problem is $$ \text{minimize}_{w, w_0} \quad \frac{1}{2} ...
2
votes
0answers
24 views

Identify the parameter causing the anomaly in a multivariate dataset

I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like ...
2
votes
1answer
23 views

Which algorithm is implemented in sklearn's SVM method?

I'd like to know which exact version of svm is implemented in slearn. The references section on sklearn's svm page cites libsvm package and a paper from 1999 which is about comparing classification ...
2
votes
1answer
5k views

Best way to train one-class SVM

Let`s say I have training data which contains 10 classes and have build a classifier using this data. When applying this classifier in real life it may encounter examples not belong to the classes ...
1
vote
1answer
43 views

Why do you need a balanced test set?

It seems to be the consensus that, if possible, both train and test set for binary classification should be balanced over the two classes, especially if using classifiers like SVM. Whilst I ...
0
votes
0answers
15 views

Applying different kernels to parts of a dataset and merging the output [duplicate]

I am trying to create a classifier using SVM on a dataset that is composed of 6 sets of data for each of my observations. When I train the SVM (rbf kernel), I get a better performance of the ...
47
votes
4answers
65k views

Comparing SVM and logistic regression

Can someone please give me some intuition as to when to choose either SVM or LR? I want to understand the intuition behind what is the difference between the optimization criteria of learning the ...
2
votes
0answers
17 views

How to prepare the training data for Support Vector Machine?

I'm currently doing some comparison of Naive Bayes Algorithm and Support Vector Machine classifying news to see each algorithm's accuracy. I already know how to prepare the training data for Naive ...
3
votes
1answer
33 views

Cost function in SVM

I've followed the Machine Learning course of Andrew Ng, and I really confuse in Support Vector Machine lecture. Regarding cost function in SVM, he said that when C is very large, the loss (error) ...

1
2 3 4 5
41