Questions tagged [svm]

Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.

Filter by
Sorted by
Tagged with
158
votes
6answers
243k views

How to draw Deep learning network architecture diagrams?

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
109
votes
12answers
118k views

SVM using scikit learn runs endlessly and never completes execution

I am trying to run SVR using scikit-learn (python) on a training dataset that has 595605 rows and 5 columns (features) while the test dataset has 397070 rows. The data has been pre-processed and ...
70
votes
2answers
9k views

Are Support Vector Machines still considered "state of the art" in their niche?

This question is in response to a comment I saw on another question. The comment was regarding the Machine Learning course syllabus on Coursera, and along the lines of "SVMs are not used so much ...
34
votes
4answers
73k views

When to use Random Forest over SVM and vice versa?

When would one use Random Forest over SVM and vice versa? I understand that ...
34
votes
2answers
45k views

How to calculate mAP for detection task for the PASCAL VOC Challenge?

How to calculate the mAP (mean Average Precision) for the detection task for the Pascal VOC leaderboards? There said - at page 11: Average Precision (AP). For the VOC2007 challenge, the interpolated ...
24
votes
2answers
45k views

Can you explain the difference between SVC and LinearSVC in scikit-learn?

I've recently started learning to work with sklearn and have just come across this peculiar result. I used the digits dataset ...
20
votes
2answers
11k views

How to increase accuracy of classifiers?

I am using OpenCV letter_recog.cpp example to experiment on random trees and other classifiers. This example has implementations of six classifiers - random trees, boosting, MLP, kNN, naive Bayes and ...
20
votes
2answers
15k views

What kinds of learning problems are suitable for Support Vector Machines?

What are the hallmarks or properties that indicate that a certain learning problem can be tackled using support vector machines? In other words, what is it that, when you see a learning problem, ...
18
votes
5answers
28k views

Choose binary classification algorithm

I have a binary classification problem: Approximately 1000 samples in training set 10 attributes, including binary, numeric and categorical Which algorithm is the best choice for this type of ...
17
votes
4answers
11k views

In SVM Algorithm, why vector w is orthogonal to the separating hyperplane?

I am a beginner on Machine Learning. In SVM, the separating hyperplane is defined as $y = w^T x + b$. Why we say vector $w$ orthogonal to the separating hyperplane?
14
votes
1answer
31k views

Intuition for the regularization parameter in SVM

How does varying the regularization parameter in an SVM change the decision boundary for a non-separable dataset? A visual answer and/or some commentary on the limiting behaviors (for large and small ...
12
votes
2answers
3k views

The differences between SVM and Logistic Regression

I am reading about SVM and I've faced to the point that non-kernelized SVMs are nothing more than linear separators. Therefore, ...
12
votes
1answer
4k views

What happens when we train a linear SVM on non-linearly separable data?

What happens when we train a basic support vector machine (linear kernel and no soft-margin) on non-linearly separable data? The optimisation problem is not feasible, so what does the minimisation ...
11
votes
2answers
2k views

Consequence of Feature Scaling

I am currently using SVM and scaling my training features to the range of [0,1]. I first fit/transform my training set and then apply the same transformation to my testing set. For example: ...
11
votes
1answer
2k views

What's the relationship between an SVM and hinge loss?

My colleague and I are trying to wrap our heads around the difference between logistic regression and an SVM. Clearly they are optimizing different objective functions. Is an SVM as simple as saying ...
10
votes
4answers
3k views

Skewed multi-class data

I have a dataset which contains ~100,000 samples of 50 classes. I have been using SVM with an RBF kernel to train and predict new data. The problem though is the dataset is skewed towards different ...
9
votes
1answer
664 views

Is there any domain where Spiking Neural Networks outperform other algorithms (non-spiking)?

I'm reading about reservoir computing techniques like Echo State Networks and Liquid State Machines. Both of the methods involve feeding inputs to a population of randomly (or not) connected spiking ...
9
votes
1answer
313 views

Using SVM as a binary classifier, is the label for a data point chosen by consensus?

I'm learning Support Vector Machines, and I'm unable to understand how a class label is chosen for a data point in a binary classifier. Is it chosen by consensus with respect to the classification in ...
9
votes
4answers
19k views

Improving accuracy of Text Classification

I am working on a text classification problem, the objective is to classify news articles to their corresponding categories, but in this case the categories are not very broad like, politics, sports, ...
9
votes
1answer
2k views

Can training label confidence be used to improve prediction accuracy?

I have training data that is labelled with binary values. I also have collected the confidence of each of these labels i.e. 0.8 confidence would mean that 80% of the human labellers agree on that ...
9
votes
1answer
964 views

Feature selection for Support Vector Machines

My question is three-fold In the context of "Kernelized" support vector machines Is variable/feature selection desirable - especially since we regularize the parameter C to prevent overfitting and ...
9
votes
1answer
1k views

sklearn - overfitting problem

I'm looking for recommendations as to the best way forward for my current machine learning problem The outline of the problem and what I've done is as follows: I have 900+ trials of EEG data, where ...
8
votes
2answers
5k views

Kernel trick explanation

In support vector machines, I understand it would be computationally prohibitive to calculate a basis function at every point in the data set. However, it is possible to find this optimal solution due ...
7
votes
1answer
205 views

Where exactly does $\geq 1$ come from in SVMs optimization problem constraint?

I've understood that SVMs are binary, linear classifiers (without the kernel trick). They have training data $(x_i, y_i)$ where $x_i$ is a vector and $y_i \in \{-1, 1\}$ is the class. As they are ...
7
votes
1answer
4k views

how to make sklearn pipeline using custom model?

I want to make a sklearn pipeline using the custom Artificial Neural Network I already have. I want to make pipeline in which input goes to ANN and its output goes to the sklearn.svm.SVC model and ...
7
votes
5answers
656 views

Where to start on neural networks

First of all I know the question may be not suitable for the website but I'd really appreciate it if you just gave me some pointers. I'm a 16 years old programmer, I've had experience with many ...
7
votes
1answer
2k 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 ...
7
votes
1answer
8k views

Multi-class classification v.s. Binary classification

A training set has five classes including: "label-A", "label-B", "label-C", "label-D", "others" But the problem ...
7
votes
1answer
174 views

Please enlighten me with Platt's SMO algorithm (for SVM)

From A_Roadmap_to_SVM_SMO.pdf, pg 12. (source: postimg.org) Assume I am using linear kernel, how will I be able to get both the first and second inner product? My guess, inner product of datapoint ...
7
votes
1answer
1k views

Custom metrics for unbalanced classes problem in RandomForest or SVM

My dataset has highly unbalanced classes ‒ foreground of 30 classes with tens of samples against background set of >100k samples. Classifying foreground class as background is quite OK, while ...
7
votes
3answers
207 views

Understanding Classifier performance on text data

I am working on a multi-label text classification problem(Total target labels 90). The data distribution has a long tail and class imbalance and around 1900k records. Currently, I am working on a ...
6
votes
1answer
5k views

How do I interpret the length-scale parameter of the RBF kernel?

According to the Scikit-Learn documentation for the RBF kernel: The length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension ...
6
votes
2answers
9k views

Predicting probability from scikit-learn SVC decision_function with decision_function_shape='ovo'

I have a multiclass SVM classifier with labels 'A', 'B', 'C', 'D'. This is the code I'm running: ...
6
votes
1answer
142 views

How are Hyperplane Heatmaps created and how should they be interpreted?

For nonlinear data, when we are using Support Vector Machines, we can use kernels such as Gaussian RBF, Polynomial, etc to achieve linearity in a different (potentially unknown to us) feature space ...
6
votes
2answers
1k views

Classifying survey response text SVM

I have 800 responses to an open-ended survey question. Each response is categorized into 3 categories based on a list of 70 categories. These categories are things like "stronger leadership", "better ...
6
votes
3answers
3k views

Linear kernel in SVM performing much worse than RBF or Poly

When trying to train a SVM on some Kaggle data, I have encountered a situation where the linear kernel fails to give any results. This doesn't make sense to me because the RBF kernel works just fine, ...
6
votes
1answer
4k views

What are the differences between SVC, NuSVC, and LinearSVC?

What are the differences between SVC, NuSVC, and LinearSVC? Please shed some light.
6
votes
1answer
153 views

Geometric Interpretation of Whether SVMs are performing well or not

I came across this research paper which contained this figure which talks about the center of mass (presumably, of the training dataset's datapoints?) and represents the solution of an SVM as ...
6
votes
1answer
141 views

Is there any conceptual relationship between 'kernel' in SVM and 'kernel' in convolution neural net?

In SVM, we have kernel function that maps an input raw data space into a higher dimensional feature space In CNN, we also have a 'kernel' mask that travels the input raw data space (image as a matrix)...
6
votes
2answers
374 views

Difference between rbfnn and svr with gaussian kernel

I try to understand the difference between radial basis neuron network and support vector regression with Gaussian Kernel. I watched Andrew Ng presentation about kernels in SVN, I have read also an ...
5
votes
4answers
213 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: ...
5
votes
2answers
5k views

Understanding the math behind SVM

I was going through this Udacity video (part of the ML nano-degree), where the math behind maximizing the length between the data clouds of two classes is done. So, the line closest to the +1 class ...
5
votes
1answer
3k views

SVM on sparse data

In this paper related to factorization machine, the author compares factorization machine (FM) with SVM. As FM performs better than SVM, it's considered state of the art for sparse data. Why SVM is ...
5
votes
2answers
6k views

Why Decision Tree boundary forms a square shape and SVM a circular/oval one?

I was going through a Udacity tutorial wherein a few data points were given and the exercise was to test which of the following models best fit the data: linear regression, decision tree, or SVM. ...
5
votes
1answer
5k views

Where is the cost parameter C in the RBF kernel in SVM?

RBF kernel using SVM depends on two parameters C and gamma. If the equation of the kernel RBF as the following: $K(X,X')= \exp(\gamma||X-X'||^2)$ In the equation I can see where can I use gamma, but ...
5
votes
3answers
274 views

Looking for a classification (?) algorithm for linearly separable but unlabeled data points

I have a dataset that is linearly separable with two lines - something like that: Now I'am looking for the right kind of algorithm to do what I guess a SVM would do with labeled data - find the ...
5
votes
2answers
3k views

What algorithms will stuck in the local minimum?

Algorithms like neural network are easily getting stuck in local minimum because the shape of the loss function (so there are parameters like momentum are designed to solve this type of problem). ...
5
votes
1answer
6k views

SVDD vs once Class SVM

Can some one please explain me what is the difference between one class SVM and SVDD(support vector data description)
5
votes
1answer
6k views

Polynomial Kernel Parameters in SVMs

In SVMs the polynomial kernel is defined as: (scale * crossprod(x, y) + offset)^degree How do the scale and offset parameters affect the model and what range should they be in? (intuitively please) ...
5
votes
2answers
2k views

Detecting over fitting of SVM/SVC

I am using 3-fold cross validation and a grid search of the C and gamma parameters for a SVC using the RBF kernel I have achieved a classification score of 84%. When testing against live data the ...

1
2 3 4 5
11