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Questions tagged [svm]

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

38 questions
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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 expression like said at 35:56 in the YouTube video.…
Ömer Yıldız
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What algorithm do SVMs use to minimize their objective function?

Support Vector Machines turn machine learning linear classification tasks into a linear optimization problems. $$ \text{minimize } J(\theta,\theta_0) = \frac1n \sum_1^n \text{HingeLoss}(\theta,\theta_0) + \frac{\lambda}{2} ||\theta||^2 $$ My…
HelloWorld
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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 way to label this new, yet unseen data? I was…
Uli Niklas
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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 the problem, but I would like to know why my own…
user3002473
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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})+\rho k(\bar{x}_{k}, \bar{x})\; \mbox{if}\;…
priyanka
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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 0110 for example. The purpose of classifier (SVM)…
Nick
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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 be bad because train data would not cover the…
Sami
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Machine Learning Algorithm Recommendation For Sensor Data

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 are as follows; x is time axis and y is sensor…
Reiso
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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 using the same? the code iam using…
user67593
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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), the support vectors lie along the supporting…
pranavps
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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 image. Please provide a detailed procedure on…
Divyat
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How to train SVM in matlab / python for MultiLabel data?

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. This is not the same as a multiclass SVM…
Tad Thompson
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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 very much on the architecture. For supposing you…
Martin Thoma
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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 values for the other dimension are real numbers…
zpavlinovic
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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? Laplacian Kernel is $$ K(x,x') = \exp\left(-\dfrac{\|…
quarantineStudy
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