Questions tagged [svm]

Questions about Support Vector Machines. SVMs are supervised learning models used for classification and regression tasks.

14 questions with no upvoted or accepted answers
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some kernel and greater margin, how this occures?

I read following notes, and couldn't get it. any idea or hint would highly appreciated. a SVM classifier using a second order polynomial kernel. The first polynomial kernel maps each input data x to ...
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52 views

Support Vectors in SVM

It might be a very basic question.$\\$ I am considering the SVM optimization problem here.$\\$ In a training set where the data is linearly separable, and we are using a hard margin (no slack allowed),...
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238 views

What is the complexity of classification with SVMs?

I'm interested in how fast SVMs can classify new data with $c \in \mathbb{N}_{\geq 2}$ classes and $n \in \mathbb{N}_{\geq 1}$ features. Example for Neural Networks For neural networks, this depends ...
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35 views

How can a classifier using Laplacian kernel achieve no error on the input samples?

If we have a sample dataset $S = \{(x_1, y_i),\dots,(x_n,y_n)\}$ where $y_i = \{0,1\}$, how can we tune $\sigma$ such that there is no error on $S$ from a classifier using the Laplacian kernel? ...
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42 views

How do we mathematically figure out if a SVM kernel function overfits?

Looking at the kernel function (Gaussian, polynomial. chi-squared, etc) how do we figure out that changing which value will cause overfitting? In my perspective, if we increase (for example) the ...
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66 views

How to mitigate the hierarchical error propagation in tree-structured classification

Suppose we have a multi-class classification problem, where the number of classes $K \geq 3$ We use a tree structure of multiple SVMs to divide and conquer the problem, with one example in the figure ...
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179 views

Computer vision training procedures: SVM/AdaBoost vs Neural Networks

With SVM, adaboost or similar alogrithms, image training sets must be cropped with specific constraints (keep image cropping ratio the same, have object tightly cropped, same resolution) In general, ...
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13 views

Perpendicular Training Vectors SVM

Claim: If we are training a hard-margin SVM on a set of perpendicular training vectors which can either be classified as "positive" or "negative," will every training vector end up ...
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20 views

Support Vectors SVM

I have read somewhere that the value of slack variables of support vectors is not 0. Does that mean the points lying in the wrong region e.g a positive point lying in the negative region will also be ...
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18 views

How to choose better performance based on margins from two kernels

In SVM, how can or cannot the margin attained by two different kernels on a single dataset be used to determine which classifier has better performance on the dataset? Can we just plot the decision ...
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20 views

Stochastic Gradient Descent for Multi-Class SVM

I'm trying to compute the optimization problem for a multi-class SVM loss function with $L2$ regularization. $\displaystyle f(W) = \frac{1}{n}\sum_{i=1}^n\sum_{c\neq y_i} \max\{0,1-w_{y_i}^Tx_i+w_c^...
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1answer
43 views

Classification accuracy based on top 3 most likely classifications

My goal is to recommend jobs to job seekers based on their skill set. Currently I'm using an SVM for this, which is outputting one prediction, e.g. "software engineer at Microsoft". However, consider ...
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171 views

(Percision & recall) Vs (Accuracy)? which one do I have to consider?

I am running several machine learning classifiers to predict something from my data. If I visualized the precision and recall tables as a result, is it enough to get clear idea about the proposed mdel?...
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252 views

Support Vector Machines vs K-Nearest Neighbors

Let's say we have trained a Support Vector Machine with a Gaussian Kernel. When we feed our model an example, it classifies it based on its similarity to landmarks (distance to examples in our ...