Questions tagged [svm]
Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.
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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 ...
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1answer
27 views
What exactly is .csv in machine learning? [on hold]
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
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1answer
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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, ...
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2answers
25 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 ...
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1answer
20 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, ...
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1answer
14 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 ...
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2answers
34 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 ...
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0answers
21 views
Support Vector Machines for Time Series Forecasting [closed]
I have dataset that has Temporal features as Day of week, Hour, Minute, Second, isWeekend and a target value (Y) that represent Number of requests
I want to use SVR to predict Y. How can I do this ...
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2answers
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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-...
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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).
...
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1answer
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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 ...
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2answers
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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 ...
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What is the difference between Kernelized extreme learning machine vs kernel ridge regression if we use Gaussian kernel?
I am getting same outcomes by using either Kernelized extreme learning machine or Kernel ridge regression in case of any kernel. However, I tested on Gaussian kernel.
As per my understanding, both ...
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1answer
22 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 ...
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0answers
21 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 ...
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1answer
20 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 ...
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1answer
68 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 ...
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support vector machine under noisy labels
I am relatively new to machine learning and had just read the SVM chapter in the introduction to statistical learning book.
I am interested in applying SVM to my data. In theory, my data is perfectly ...
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1answer
16 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,...
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2answers
39 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.
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0answers
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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 ...
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36 views
Why precision increases with recall?
I'm working on multi-class classification problem. There are ~1600 samples; 47 features and 9 imbalanced classes. The smallest class includes just 17 samples whereas the larges one over 600 (classes ...
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1answer
31 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 ...
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1answer
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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? ...
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2answers
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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 ...
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1answer
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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 ...
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Primal vs Dual SVM Problem for linearly separable data
Given a linearly separable data where i don't need to use kernels. Is there any need to use the dual form? In other words, is primal form enough?
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1answer
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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 ...
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0answers
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One-Vs-Rest classifier implementation using SVR
In One-Vs-Rest classifier it is mentioned that in estimator it requires fit and one of decision_function or predict_proba. Since SVR doesn't have the latter part can we implement One-Vs-Rest ...
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Support Vector Machines VS LSTMs: How well it is justifiable to use LSTM for its Generalization properties?
The question is pretty straightforward, How well one can justify using LSTMs(Neural Networks) for text classification task in terms of "Generalization" compared to classic support vector machines(SVM) ...
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21 views
Geometry of Margin and Normal Vector in SVM
In Support Vector Machine , Can somebody tell me using geometry how the length of the normal vector is proportional to the width of margin. ?
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SVM: How to find support vectors?
I am newbie to SVM.
As I believe, our main goals in SVM are
1) to maximize the margin between the decision boundary and data points and
2) find the support vectors
So what I understood is we use ...
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1answer
47 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 ...
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1answer
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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 ...
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2answers
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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, ...
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1answer
86 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 ...
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1answer
24 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
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0answers
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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 ...
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Why SVM with zero number of support vector is yielding good results?
I have implemented a multi-kernel SVDD for one-class classification. It is yielding good results in the case of the number of support vector is zero. I have tested on various datasets like Iris, Germa-...
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0answers
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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 ...
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2answers
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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 ...
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1answer
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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 ...
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0answers
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Is it possible to perform transfer learning using kernel ridge regression or any non-iterative approach of machine leaning techniques?
kernel ridge regression can be used for regression or classification task. How can we perform transfer learning for kernel ridge regression?
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2answers
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Support Vector classifier perform well with input features rather than transformed features in contrast to ANN-BP, random forest (other classifiers)
I am working on stock data with 5 raw features (OHLCV). Using few transformations used by technical analysts, have created 20 more features giving different kinds of indications.
When trying to ...
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1answer
34 views
Logistic Regression or regression SVM for probability of outcome
I am working on a prediction question: what's the percentage of Y = 1 using a number of features?
The output Y values I have for training are in binary. In this case, should the prediction be ...
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2answers
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How can I find anomalies in each row of data?
I have some reported data I want to spot anomalies on. The columns are a facility name then monthly reports of that given facility.
...
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0answers
22 views
heterogeneous input features for estimators in sklearn.ensemble.VotingClassifier
I would like to do ensemble on my model.
Two of them are SVM and XGBoosting. SVM could not tolerate null value and XGB can do it. So I have different features for each of them. but when ...
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0answers
76 views
Interpreting rawPrediction from Spark ML LinearSVC
I am using Spark ML's LinearSVC in a binary classification model. The transform method creates two columns, prediction and ...
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1answer
127 views
Poor performance of SVM after training for rare events
I found out that Weighted SVM is a classification approach to handle class imbalance problem. My data set is highly imbalanced with rare event (minority class, labeled as 1) and the majority class (...
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RBF NN vs RBF SVM: Which approach is more suitable or exactly right for low features/training_samples ratio?
In my labeled dataset, there are 80 features, used to classify whether a TCP packet is malicious or not, taken from UNB-ICSX 2017. The packet is labeled either benign or is malignant in the form of ...
