Questions tagged [svm]

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

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3answers
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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 ...
5
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1answer
65 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 ...
4
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0answers
164 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 ...
3
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1answer
31 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 ...
3
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1answer
511 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})+\...
2
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1answer
430 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 ...
2
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2answers
149 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 ...
2
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1answer
346 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 ...
2
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1answer
181 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 ...
2
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0answers
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),...
2
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0answers
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 ...
1
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1answer
160 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....
1
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1answer
134 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 ...
1
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0answers
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? ...
1
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0answers
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 ...
1
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0answers
54 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 ...
1
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1answer
300 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 ...
1
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0answers
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 ...
1
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0answers
182 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
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, ...
0
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1answer
837 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 ...
0
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1answer
32 views

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 ...
0
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1answer
152 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
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 ...
0
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1answer
36 views

What is a Black Box attack against Machine Learning algorithms?

And is there an attack strategy that you can use to approximate the architecture of a machine learning system with the knowledge of class labels and some data points?
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0answers
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 ...
0
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0answers
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 ...
0
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0answers
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^...
0
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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 ...
0
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1answer
49 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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0answers
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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0answers
253 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 ...