Questions tagged [svm]
Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.
0
votes
0answers
11 views
Support Vector Machine
What is the difference between SVM classification error, SVM margin error, and SVM total error ? Is there any clear definition for them ? And what is C parameter in SVM ? Its totally confusing me !!!
3
votes
0answers
17 views
Scaling does not speed up the SVM model
I tried to standardize the training data with samples of 629,145 rows and 24 features:
...
1
vote
1answer
24 views
Why am I getting very different results between SVC, LinearSVC and Naive Bayes?
I am doing classification by using bag-of-words model. The goal is to locate users based on their tweets. Splitted the data as 80% training and 20% test.
I did experiments with sklearn's SVC and ...
1
vote
0answers
5 views
General way of constructing adjacency matrix in Laplacian SVM semi-supervised technique
I am trying to implement a Laplacian SVM classifier (trained in primal) using algorithm from this paper.
I would like to know what is the most common way of constructing adjacency matrix and the most ...
1
vote
0answers
17 views
How to choose the support vectors after minimizing the objective function?
I'm training a SVM that uses the following objective function:
$$
\frac{1}{2}\sum_i{\sum_j{\alpha_i\alpha_j t_i t_j \mathcal{K}(\vec{x_i}, \vec{x_j})}} - \sum_i{\alpha_i}
$$
The objective function ...
1
vote
0answers
20 views
what is fuzzy svm?
I have to solve this question for my homework but I don't get how to formulate svm to FSVM. can someone please guide me?
What is your idea to have a model of SVM classifier in which instances can ...
1
vote
0answers
14 views
SVM Cost function change to improve its computational efficiency
While listening to Andrew Ng's course of Machine Learning he said that the
SVM's cost function term $\frac{\Theta^T\Theta}{2}$ is usually changed to $\frac{\Theta^TM\Theta}{2}$, where matrix $M$ ...
4
votes
4answers
134 views
Is the prediction algorithm absolutely the same for all linear classifiers?
Is the prediction algorithm absolutely the same for all linear classifiers and linear regression algorithms?
As known, any linear classifier can be described as: ...
1
vote
1answer
65 views
How to feed data to Machine Learning Model?
I'm working on an SVM model as my college project. And the goal is to identify whether a tumor is benign or malignant. I'm implementing the model in Python. I found the data set from Gene Expression ...
1
vote
1answer
32 views
Scaling label encoded values for Linear Algorithms
I have encoded categorical variables to numerical values. As we know that for feeding values to Linear Algorithms like SVM or KNN, we scale the values for columns having large variations.
I have ...
2
votes
1answer
18 views
RBF kernel can classify two classes as in figure?
As you can see, I have some points (belonging to red and blue class), and I would to use an RBF kernel but I think that an RBF kernel can make points linearly separable only if they are located in ...
1
vote
1answer
25 views
test accuracy of text classification is too less
I have a data set of movies and their subtitles.My task is to classify them based on their ratings-[R,NR,PG,PG-13,G].I have 13 examples for each class.
I preprocessed the subtitles in the following ...
2
votes
1answer
43 views
SVM vs RVM, when to use what?
I'm currently working on a project where I'm supposed to compare the efficiency of SVM vs RVM, there seems to be a lot of information to be gathered about RVM whereas I find rather old documents about ...
2
votes
0answers
19 views
Structured Support Vector Machine (Joint Feature Map)
I'm studying Structured Support Vector Machine. (https://en.wikipedia.org/wiki/Structured_support_vector_machine)
The theory's clear, but I need a tangible example to make everything more concrete.
...
1
vote
1answer
18 views
Context classification problem
I have a bunch of articles about science from a certain website. When a new article is published, I want to determine if that article is really talking about science (and not politics for example). ...
1
vote
1answer
36 views
Example of a problem with structured output labels
I'm studying SSVM (Structured SVMs).
On my book is stated that Structured SVM is an extension of the SVM, in which
Each sample is assigned to a structured output label z ∈ K, e.g. partitions,
...
2
votes
0answers
21 views
I have data of some movies and their subtitles.I want to classify them based on their ratings
I will convert the subtitles into vectors and use them as features to classify the movies into different categories based on their ratings.The problem that I am facing is my feature vector is much ...
2
votes
0answers
15 views
solution of quadratic optimization in support vector machines
In support vector machines, the minimization problem with inequality constraints can be converted to a minimization problem of Lagrange multipliers with equality constraints by KKT condition and ...
1
vote
0answers
15 views
Are there any good solutions for putting a radial basis kernel support vector machine into production?
Are there any good options for a radial basis kernel SVM where I can serialize the model to store and later deserialize and evaluate? I'm using H2O for some other things and it supports SVM but no ...
1
vote
1answer
39 views
What exactly is .csv in machine learning? [closed]
I already have dataset of dogs and cats , so do i need to make .csv file or can i directly use the dataset for classification
1
vote
1answer
22 views
How to break down large SVM classification model?
I have a classification problem with large number of classes: feature set is 512 Dimension, number of classes are around 3000. This is a face identification problem. (identify among 3000 celebrities, ...
1
vote
2answers
36 views
Poor performance for unbalanced dataset
Consider a dataset A which has examples for training in a binary classification problem. I have used SVM and applied the weighted method (in MATLAB) since the ...
2
votes
1answer
33 views
svm optimization problem
Suppose we have the dataset:
{(3,1),(3-1),(6,1),(6,-1)} {(1,0),(0,1),(0,-1),(-1,0)}
the first set represent the positive label, and de second the negative.
I want manually find the support vectors, ...
1
vote
1answer
19 views
Feature selection/reduction techinique for combination of features in image processing
I have a combination of features extracted from 3 descriptors, namely GLCM based feaures(correlation, homogeneity,energy and contrast ), Local binary patterns (256) and discrete wavelet transform ...
2
votes
2answers
124 views
Multi Class Classification on large dataset with over 600 classes
I'm trying to train a text data for multi class classification which comprises of 1 Million rows. After cleaning the data, I'm using a sparse matrix of Word2Vec features (Feature size is 300)
The ...
2
votes
2answers
27 views
Minimum numbers of support vectors
I'm trying to understand the concept of SVM.
Consider linearly separable data $\{(x_i , y_i )\}_{i=1}^n , x_i \in \mathbb R^d , y_i \in \{−1, 1\}. \text{Let}\ \ \{x | w^T x + b = 0\}$ be the margin-...
1
vote
0answers
7 views
Train OneVsRest svms separately
I need to perform classfication of hundreds of classes. New classes arrive regularly. I also have some large training set (thousands of samples).
...
1
vote
1answer
21 views
How to aggregate face embeddings of all photos of the same person?
I am classifying about 3000 thousand people's faces using FaceNet. Each person has about 100 photos.
FaceNet first calculates a face embedding ( a feature vector) for each photo. So each person has ...
1
vote
2answers
85 views
What is the difference between SVM and logistic regression?
While reading the book by Aurelien Geron, I noticed that both logistic regression and SVM predict classes in exactly the same way, so I suspect there must be something that I am missing. In the ...
2
votes
1answer
60 views
How regularization parameter in SVM affects hyperplane parameters
While learning the SVM classification I came across the regularization parameter $\lambda$:
$F(w,b) = \left\lVert w\right\lVert_2^2 +\lambda \sum_{i=1}^n max(0,1-y_i(w^Tx_i +b)).$
So from what I ...
2
votes
0answers
30 views
Non-linear Support Vector Regression issue - Sklearn Python 3.6
I am fairly new to Sklearn and machine learning and have encountered an issue when using SVR with an RBF kernel. Below is my predicted data compared directly with my real data:
I do not know what I ...
0
votes
1answer
23 views
prediction for a linear sum
I am learning about SVMs in particular linear SVMs through many questions here. However, one problem i faced is that there seems to be no indepth explanation on how does linear SVM works in terms of ...
1
vote
1answer
79 views
SVM model classifying into one class only, after standardization
I'm trying to use SVM in R (e1071 package) to classify samples as normal or tumor. I have two separate data sets - Training (~50 samples, 100 features) and Test (~60 samples). These data sets are ...
2
votes
1answer
17 views
How to include class features to linear SVM
I am planning to do a simple classification with a linear SVM. One feature I have is another classification of some sort done previously.
Can I just use this class feature as a 1-hot encoded array? So,...
0
votes
2answers
42 views
Using multiple machine learning algorithms together [closed]
I'm kinda new to machine learning and wanted to know if we could use multiple machine learning algorithms, for example, SVM and backpropagation together to solve a particular problem.
1
vote
0answers
17 views
SVM - why does scaling the parameters w and b result in nothing meaningful?
The functional margin tells us how confident the SVM is in it's classification, for big values it's better than for small ones. Now the question that bugs me is as to why we can't make ...
0
votes
1answer
62 views
Linear SVM in matlab and python giving different results
I have a particular dataset on which I am getting different results when using a linear SVM in matlab and sklearn toolbox.
The data has been normalized in matlab and imported into python from a mat ...
0
votes
2answers
29 views
What advantage does Guassian kernel have than any other kernels, such as linear kernel, polynomial kernel and so on?
Guassian kernel is so important in SVM as we know. The parameter gamma is designed for this kind of kernel. My question is what makes Guassian kernel so unique? ...
3
votes
2answers
99 views
How can I use two different datasets as a training model for svm
I know that you're supposed to scale your test data using the parameters (mean and stdev) from your training data. This is relatively simple; but what if the number of samples is limited in one ...
0
votes
1answer
24 views
Post training classifier configuration
I have a behaviours vector representing some identity. I need to binary classify [malicious or benign] each instance [ideally with a normalised severity score].
For that I can use a variety of linear ...
2
votes
1answer
23 views
Doubt with SVM math
I have a question about SVM that some of you may help me with…
I know that y(xi), by convention, would be -1 or 1 depending on which class the Xi belongs to.
But I don't fully understand why it's ...
1
vote
1answer
55 views
Tuning svm and cart hyperparameters
I am trying to optimize the hyperparameters of SVM and CART with tune() function of e1071 R package, but I have a doubt.
Should I tune the parameters on the training data, fit the model on the ...
1
vote
1answer
30 views
scikit-learn: High / low value for C in SVM
I'm playing with scikit-learn. Looking into the user guide and documentation they say:
A low C makes the decision surface smooth, while a high ...
0
votes
2answers
96 views
Relationship between train and test error
I have some specific questions for which I could not extract answers from books. Therefore, I ask for help here and shall be extremely grateful for an intuitive explanation if possible.
In general, ...
5
votes
1answer
164 views
How to plot mean_test score and mean_train score of GridSearchCV
How to plot mean_train_score and mean_test_score values in GridSearchCV for ...
2
votes
1answer
29 views
How to plot train test error for classification models like Support Vector Classification(SVC)
How to plot train test error for classification models like Support Vector Classification(SVC). I am using SVC from sklearn module, not able to get train and test errors to plot
1
vote
0answers
69 views
How to make a hybrid ARIMA and SVMs model in R
I want to combine 2 awesome models for data prediction / forecast - an ARIMA and an SVMs model, and thus I want to reduce standard error for the hybrid model. Currently, here's a graph showing them in ...
1
vote
0answers
29 views
Understanding Support Verctor Regression (SVR)
This question also asked on another StackExchange with Bounty. Question here.
I'm working with SVR, and using this resource. Erverything is super clear, with epsilon intensive loss function (from ...
2
votes
2answers
122 views
Sklearn SVM - how to get a list of the wrong predictions?
I am not an expert user. I know that I can obtain the confusion matrix, but I would like to obtain a list of the rows that have been classified in a wrong way in order to study them after ...
0
votes
1answer
135 views
GPS route matching
We have a mobile application which records many of the sensors on a users mobile to a database (time,GPS location, activity (e.g. walking,still), network connectivity status) etc.
The user is ...
