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Questions tagged [svm]

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

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SVM with a priori information about class probabilities

Given are two 2-d sets, each with its own bivariate normal distribution. I need to build an SVM classifier. The a priori probabilities of each class corresponds to the size of its set (20/50 for the ...
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1answer
44 views

How to understand a equation related to speaker recognition?

This question refers to the following paper: Support Vector Machines for Speaker and Language Recognition, W. M. Campbell, J. P. Campbell, D. A. Reynolds, E. Singer, P. A. Torres-Carrasquillo, ...
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1answer
31 views

k-means clustered data: how to label newly incoming data

I have a data set with labels that were produced by a $k$-means clustering algorithm. Now there is some data (with the same data structure) from another source and I wonder what is the most sensible ...
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1answer
25 views

Why doesn't this derivation of the margin in a SVM give the correct result?

I'm trying to derive the optimization objective for an SVM (namely $1/\|w\|$), but I'm running into a little trouble. I've already read this question, which has certainly offered a lot of insight into ...
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1answer
56 views

How to find max margin for non-separable SVM?

I am new to Machine Learning. Suppose a training set of positive (square) and negative (circle) points is given like: Obviously there would be no nice linear separator of positive and negative points....
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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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(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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3answers
793 views

Why do we try to maximize Lagrangian in SVMs?

I was learning about support vector machines from MIT OpenCourseWare. I figured it out. I understand why we try to minimize $\frac{1}{2} w^2$. I just did not get why we try to maximize Lagrange ...
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1answer
283 views

How to tackle different sample size in the training set in SVM

I have to train a SVM for a classification problem. I have some strings that are the paths in a deterministic finite automata (DFA). If the alphabet is -01- then possible strings are 011101110 or ...
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2answers
136 views

Overfitting in Machine Learning Algorithms

I am new in the ML. I know that overfitting is memorizing the data while training. Like in Neural Network, if we make lots of layers and lots of hidden nodes, we can memorize all the data, but it can ...
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0answers
48 views

Machine Learning Algorithm Recommendation For Sensor Data [closed]

I would like to classify data coming from a sensor. In the literature Hidden Markov Model and SVM are used, but I would like to improve results with another methods.The picture how data and classes ...
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1answer
226 views

SVM with different length features

I need to train SVM with 2 different features..the problem is one feature is the HOG with length 144 and other an RGB value with length 3.. Can i combine these two features to train SVM and test ...
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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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1answer
309 views

How to use PHOG and LPQ features for Emotion Recognition?

I have a database that consists of PHOG and LPQ features for each image. Now, I wish to train an SVM on these features for emotion recognition i.e SVM classifies the images on basis of emotion in the ...
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135 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 ...
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0answers
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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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0answers
158 views

How to train SVM in matlab / python for MultiLabel data? [closed]

I am training a problem such that my output (y) could be more than one class. For example, the SVM could say, this input vector is class 1, but it could also say, this input vector is classes 1 AND 5. ...
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0answers
210 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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1answer
80 views

SVM Maximizing Margin

I am trying to make sense of the following: Taken from the MIT website. So I understand that we want to maximize the distance between the planes H0 and H1, where H0 is defined as ...
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1answer
139 views

Featurizing images of different dimensions

I'm building a non linear svm for images to solve a classification problem with domain {0, 1} and I'm currently doing featurization. What I want to do is create 3 features for each pixel representing ...
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0answers
162 views

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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1answer
421 views

Kernel Perceptron vs Polynomial Perceptron

I was looking at Support Vector machines (SVM) kernels. Looking at Polynomial Kernel and Kernel Perceptron I was curious how they differ? Work Done Polynomial Kernel: $d_{k+1}(x)=d_{k}(\bar{x})+\...
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176 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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1answer
558 views

Transformation from one feature space to another

I have found the following example: As an example consider the case when the input space $ {\mathcal{X}}$ consists of images of $ 16\times 16$ pixels, i.e. $ 256$ dimensional vectors, and we ...
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1answer
148 views

Feature values range

Suppose I am about to use SVM for learning a classification or ranking function. Suppose that my feature vectors are two dimensional and that values for one dimension are, say, natural numbers and the ...