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   • Support vector machine in Machine Learning

     In this article, we are going to discuss the support vector machine in machine learning. We will also cover the advantages and disadvantages and application for the same. Let’s discuss them one by one. 

     Support Vector Machines : Support vector machine is a supervised learning system and is used for classification and regression problems. Support vector machine is extremely favored by many as it produces notable correctness with less computation power. It is mostly used in classification problems. We have three types of learning: supervised, unsupervised, and reinforcement learning. A support vector machine is a selective classifier formally defined by dividing the hyperplane. Given labeled training data the algorithm outputs best hyperplane which classified new examples. In two-dimensional space, this hyperplane is a line splitting a plane into two parts where each class lies on either side. The intention of the support vector machine algorithm is to find a hyperplane in an N-dimensional space that separately classifies the data points. 

     Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for classification and regression tasks. The main idea behind SVM is to find...

     Check all answers on Geeksforgeeks

     1/5

   • Support vector machine in Machine Learning

     In this article, we are going to discuss the support vector machine in machine learning. We will also cover the advantages and disadvantages and application for the same. Let’s discuss them one by one. 

     Support Vector Machines : Support vector machine is a supervised learning system and is used for classification and regression problems. Support vector machine is extremely favored by many as it produces notable correctness with less computation power. It is mostly used in classification problems. We have three types of learning: supervised, unsupervised, and reinforcement learning. A support vector machine is a selective classifier formally defined by dividing the hyperplane. Given labeled training data the algorithm outputs best hyperplane which classified new examples. In two-dimensional space, this hyperplane is a line splitting a plane into two parts where each class lies on either side. The intention of the support vector machine algorithm is to find a hyperplane in an N-dimensional space that separately classifies the data points. 

     Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for classification and regression tasks. The main idea behind SVM is to find...

     • Support vector machine works comparably well when there is an understandable margin of dissociation between classes.
     • It is more productive in high-dimensional spaces.
     • It is effective in instances where the number of dimensions is larger than the number of specimens.
     • Support vector machine is comparably memory systematic. Support Vector Machine (SVM) is a powerful supervised machine learning algorithm with several advantages. Some of the main advantages of SVM include:
     • Handling high-dimensional data: SVMs are effective in handling high-dimensional data, which is common in many applications such as image and text classification.
     • Handling small datasets: SVMs can perform well with small datasets, as they only require a small number of support vectors to define the boundary.
     • Modeling non-linear decision boundaries: SVMs can model non-linear decision boundaries by using the kernel trick, which maps the data into a higher-dimensional space where the data becomes linearly separable.
     • Robustness to noise: SVMs are robust to noise in the data, as the decision boundary is determined by the support vectors, which are the closest data points to the boundary.
     • Generalization: SVMs have good generalization performance, which means that they are able to classify new, unseen data well.
     • Versatility: SVMs can be used for both classification and regression tasks, and it can be applied to a wide range of applications such as natural language processing, computer vision, and bioinformatics.
     • Sparse solution: SVMs have sparse solutions, which means that they only use a subset of the training data to make predictions. This makes the algorithm more efficient and less prone to overfitting.
     • Regularization: SVMs can be regularized, which means that the algorithm can be modified to avoid overfitting.
     • Support vector machine algorithm is not acceptable for large data sets.
     • It does not execute very well when the data set has more sound i.e. target classes are overlapping.
     • In cases where the number of properties for each data point outstrips the number of training data specimens, the support vector machine will underperform.
     • As the support vector classifier works by placing data points, above and below the classifying hyperplane there is no probabilistic clarification for the classification.Support Vector Machine (SVM) is a powerful supervised machine learning algorithm, but it also has some limitations and disadvantages. Some of the main disadvantages of SVM include:
     • Computationally expensive: SVMs can be computationally expensive for large datasets, as the algorithm requires solving a quadratic optimization problem.
     • Choice of kernel: The choice of kernel can greatly affect the performance of an SVM, and it can be difficult to determine the best kernel for a given dataset.
     • Sensitivity to the choice of parameters: SVMs can be sensitive to the choice of parameters, such as the regularization parameter, and it can be difficult to determine the optimal parameter values for a given dataset.
     • Memory-intensive: SVMs can be memory-intensive, as the algorithm requires storing the kernel matrix, which can be large for large datasets.
     • Limited to two-class problems: SVMs are primarily used for two-class problems, although multi-class problems can be solved by using one-versus-one or one-versus-all strategies.
     • Lack of probabilistic interpretation: SVMs do not provide a probabilistic interpretation of the decision boundary, which can be a disadvantage in some applications.
     • Not suitable for large datasets with many features: SVMs can be very slow and can consume a lot of memory when the dataset has many features.
     • Not suitable for datasets with missing values: SVMs requires complete datasets, with no missing values, it can not handle missing values.
     • Face observation – It is used for detecting the face according to the classifier and model.
     • Text and hypertext arrangement – In this, the categorization technique is used to find important information or you can say required information for arranging text.
     • Grouping of portrayals – It is also used in the Grouping of portrayals for grouping or you can say by comparing the piece of information and take an action accordingly.
     • Bioinformatics – It is also used for medical science as well like in laboratory, DNA, research, etc.
     • Handwriting remembrance – In this, it is used for handwriting recognition.
     • Protein fold and remote homology spotting – It is used for spotting or you can say the classification class into functional and structural classes given their amino acid sequences. It is one of the problems in bioinformatics.
     • Generalized predictive control(GPC) – It is also used for Generalized predictive control(GPC) for predicting and it relies on predictive control using a multilayer feed-forward network as the plants linear model is presented.
     • Support Vector Machine (SVM) is a type of supervised machine learning algorithm that can be used for classification and regression tasks. The idea behind SVM is to find the best boundary (or hyperplane) that separates the different classes of data. This boundary is chosen in such a way that it maximizes the margin, which is the distance between the boundary and the closest data points from each class, also known as support vectors.
     • In the case of classification, the goal is to find a boundary that separates the different classes of data as well as possible. The input data is plotted in a high-dimensional space (with as many dimensions as the number of features), and the SVM algorithm finds the best boundary that separates the classes.
     • In the case of regression, the goal is to find the best hyperplane that fits the data. Similar to classification, the data is plotted in a high-dimensional space, but instead of trying to separate the classes, the algorithm is trying to fit the data with the best hyperplane.
     • One of the main advantages of SVM is that it works well in high-dimensional spaces and it’s relatively memory efficient. it also able to handle non-linearly separable data by transforming them into a higher dimensional space where they become linear separable, this is done by using kernel trick.
     • SVMs are not just limited to linear boundaries, it could also handle non-linear boundaries by using kernel functions that allow us to map the input data into a higher-dimensional space where it becomes linearly separable. The most commonly used kernel functions are linear, polynomial and radial basis functions (RBF).
     • SVMs are popular in various applications such as image classification, natural language processing, bioinformatics, and more.
     • Facial Expression Classification – Support vector machines (SVMs) is a binary classification technique. The face Expression Classification model determines the precise face expression by modeling differences between two facial images. Validation techniques include the leave-one-out methods and the K-fold test methods.
     • Speech Recognition – The transcription of speech into text is called speech recognition. Mel Frequency Cepstral Coefficients (MFCC)-based features are used to train Support Vector Machines (SVM), which are used for figuring out speech. Speech recognition is a challenging classification problem that is categorized using a variety of mathematical techniques, including support vector machines, pattern recognition techniques, etc.

     Check all answers on Geeksforgeeks

     2/5

   • Support vector machine in Machine Learning

     In this article, we are going to discuss the support vector machine in machine learning. We will also cover the advantages and disadvantages and application for the same. Let’s discuss them one by one. 

     Support Vector Machines : Support vector machine is a supervised learning system and is used for classification and regression problems. Support vector machine is extremely favored by many as it produces notable correctness with less computation power. It is mostly used in classification problems. We have three types of learning: supervised, unsupervised, and reinforcement learning. A support vector machine is a selective classifier formally defined by dividing the hyperplane. Given labeled training data the algorithm outputs best hyperplane which classified new examples. In two-dimensional space, this hyperplane is a line splitting a plane into two parts where each class lies on either side. The intention of the support vector machine algorithm is to find a hyperplane in an N-dimensional space that separately classifies the data points. 

     Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for classification and regression tasks. The main idea behind SVM is to find...

     • Support vector machine works comparably well when there is an understandable margin of dissociation between classes.
     • It is more productive in high-dimensional spaces.
     • It is effective in instances where the number of dimensions is larger than the number of specimens.
     • Support vector machine is comparably memory systematic. Support Vector Machine (SVM) is a powerful supervised machine learning algorithm with several advantages. Some of the main advantages of SVM include:
     • Handling high-dimensional data: SVMs are effective in handling high-dimensional data, which is common in many applications such as image and text classification.
     • Handling small datasets: SVMs can perform well with small datasets, as they only require a small number of support vectors to define the boundary.
     • Modeling non-linear decision boundaries: SVMs can model non-linear decision boundaries by using the kernel trick, which maps the data into a higher-dimensional space where the data becomes linearly separable.
     • Robustness to noise: SVMs are robust to noise in the data, as the decision boundary is determined by the support vectors, which are the closest data points to the boundary.
     • Generalization: SVMs have good generalization performance, which means that they are able to classify new, unseen data well.
     • Versatility: SVMs can be used for both classification and regression tasks, and it can be applied to a wide range of applications such as natural language processing, computer vision, and bioinformatics.
     • Sparse solution: SVMs have sparse solutions, which means that they only use a subset of the training data to make predictions. This makes the algorithm more efficient and less prone to overfitting.
     • Regularization: SVMs can be regularized, which means that the algorithm can be modified to avoid overfitting.
     • Support vector machine algorithm is not acceptable for large data sets.
     • It does not execute very well when the data set has more sound i.e. target classes are overlapping.
     • In cases where the number of properties for each data point outstrips the number of training data specimens, the support vector machine will underperform.
     • As the support vector classifier works by placing data points, above and below the classifying hyperplane there is no probabilistic clarification for the classification.Support Vector Machine (SVM) is a powerful supervised machine learning algorithm, but it also has some limitations and disadvantages. Some of the main disadvantages of SVM include:
     • Computationally expensive: SVMs can be computationally expensive for large datasets, as the algorithm requires solving a quadratic optimization problem.
     • Choice of kernel: The choice of kernel can greatly affect the performance of an SVM, and it can be difficult to determine the best kernel for a given dataset.
     • Sensitivity to the choice of parameters: SVMs can be sensitive to the choice of parameters, such as the regularization parameter, and it can be difficult to determine the optimal parameter values for a given dataset.
     • Memory-intensive: SVMs can be memory-intensive, as the algorithm requires storing the kernel matrix, which can be large for large datasets.
     • Limited to two-class problems: SVMs are primarily used for two-class problems, although multi-class problems can be solved by using one-versus-one or one-versus-all strategies.
     • Lack of probabilistic interpretation: SVMs do not provide a probabilistic interpretation of the decision boundary, which can be a disadvantage in some applications.
     • Not suitable for large datasets with many features: SVMs can be very slow and can consume a lot of memory when the dataset has many features.
     • Not suitable for datasets with missing values: SVMs requires complete datasets, with no missing values, it can not handle missing values.
     • Face observation – It is used for detecting the face according to the classifier and model.
     • Text and hypertext arrangement – In this, the categorization technique is used to find important information or you can say required information for arranging text.
     • Grouping of portrayals – It is also used in the Grouping of portrayals for grouping or you can say by comparing the piece of information and take an action accordingly.
     • Bioinformatics – It is also used for medical science as well like in laboratory, DNA, research, etc.
     • Handwriting remembrance – In this, it is used for handwriting recognition.
     • Protein fold and remote homology spotting – It is used for spotting or you can say the classification class into functional and structural classes given their amino acid sequences. It is one of the problems in bioinformatics.
     • Generalized predictive control(GPC) – It is also used for Generalized predictive control(GPC) for predicting and it relies on predictive control using a multilayer feed-forward network as the plants linear model is presented.
     • Support Vector Machine (SVM) is a type of supervised machine learning algorithm that can be used for classification and regression tasks. The idea behind SVM is to find the best boundary (or hyperplane) that separates the different classes of data. This boundary is chosen in such a way that it maximizes the margin, which is the distance between the boundary and the closest data points from each class, also known as support vectors.
     • In the case of classification, the goal is to find a boundary that separates the different classes of data as well as possible. The input data is plotted in a high-dimensional space (with as many dimensions as the number of features), and the SVM algorithm finds the best boundary that separates the classes.
     • In the case of regression, the goal is to find the best hyperplane that fits the data. Similar to classification, the data is plotted in a high-dimensional space, but instead of trying to separate the classes, the algorithm is trying to fit the data with the best hyperplane.
     • One of the main advantages of SVM is that it works well in high-dimensional spaces and it’s relatively memory efficient. it also able to handle non-linearly separable data by transforming them into a higher dimensional space where they become linear separable, this is done by using kernel trick.
     • SVMs are not just limited to linear boundaries, it could also handle non-linear boundaries by using kernel functions that allow us to map the input data into a higher-dimensional space where it becomes linearly separable. The most commonly used kernel functions are linear, polynomial and radial basis functions (RBF).
     • SVMs are popular in various applications such as image classification, natural language processing, bioinformatics, and more.
     • Facial Expression Classification – Support vector machines (SVMs) is a binary classification technique. The face Expression Classification model determines the precise face expression by modeling differences between two facial images. Validation techniques include the leave-one-out methods and the K-fold test methods.
     • Speech Recognition – The transcription of speech into text is called speech recognition. Mel Frequency Cepstral Coefficients (MFCC)-based features are used to train Support Vector Machines (SVM), which are used for figuring out speech. Speech recognition is a challenging classification problem that is categorized using a variety of mathematical techniques, including support vector machines, pattern recognition techniques, etc.

     Check all answers on Geeksforgeeks

     3/5

   • Support Vector Machine kernel types

     Top Answer

     Answered Nov 18, 2015 · 26 votes

     Let us start from the beggining. Support vector machine is a linear model and it always looks for a hyperplane to separate one class from another. I will focus on two-dimensional case because it is easier to comprehend and - possible to visualize to give some intuition, however bear in mind that this is true for higher dimensions (simply lines change into planes, parabolas into paraboloids etc.).

     Kernel in very short words

     What kernels do is to change the definition of the dot product in the linear formulation. What does it mean? SVM works with dot products, for finite dimension defined as <x,y> = x^Ty = SUM_{i=1}^d x_i y_i. This more or less captures similarity between two vectors (but also a geometrical operation of projection, it is also heavily related to the angle between vectors). What kernel trick does is to change each occurence of <x,y> in math of SVM into K(x,y) saying "K is dot product in SOME space", and there exists a mapping f_K for each kernel, such that K(x,y)=<f_K(x), f_K(y)> the trick is, you do not use f_K directly, but just compute their dot products, which saves you tons of time (sometimes - infinite amount, as f_K(x) might have infinite number of dimensions). Ok, so what it meas for us? We still "live" in the space of x, not f_K(x). The result is quite nice - if you build a hyperplane in space of f_K, separate your data, and then look back at space of x (so you might say you project hyperplane back through f_K^{-1}) you get non-linear decision boundaries! Type of the boundary depends on f_K, f_K depends on K, thus, choice of K will (among other things) affect the shape of your boundary.

     Linear kernel

     Here we in fact do not have any kernel, you just have "normal" dot product, thus in 2d your decision boundary is always line.

     As you can see we can separate most of points correctly, but due to the "stiffness" of our assumption - we will not ever capture all of them.

     Poly

     Here, our kernel induces space of polynomial combinations of our features, up to certain degree. Consequently we can work with slightly "bended" decision boundaries, such as parabolas with degree=2

     As you can see - we separated even more points! Ok, can we get all of them by using higher order polynomials? Lets try 4!

     Unfortunately not. Why? Because polynomial combinations are not flexible enough. It will not "bend" our space hard enough to capture what we want (maybe it is not that bad? I mean - look at this point, it looks like an outlier!).

     RBF kernel

     Here, our induced space is a space of Gaussian distributions... each point becomes probability density function (up to scaling) of a normal distribution. In such space, dot products are integrals (as we do have infinite number of dimensions!) and consequently, we have extreme flexibility, in fact, using such kernel you can separate everything (but is it good?)

     Rough comparison

     Ok, so what are the main differences? I will now sort these three kernels under few measures

     • time of SVM learning: linear < poly < rbf
     • ability to fit any data: linear < poly < rbf
     • risk of overfitting: linear < poly < rbf
     • risk of underfitting: rbf < poly < linear
     • number of hyperparameters: linear (0) < rbf (2) < poly (3)
     • how "local" is particular kernel: linear < poly < rbf

     So which one to choose? It depends. Vapnik and Cortes (inventors of SVM) supported quite well the idea that you always should try to fit simpliest model possible and only if it underfits - go for more complex ones. So you should generally start with linear model (kernel in case of SVM) and if it gets really bad scores - switch to poly/rbf (however remember that it is much harder to work with them due to number of hyperparameters)

     All images done using a nice applet on the site of libSVM - give it a try, nothing gives you more intuition then lots of images and interaction :-) https://www.csie.ntu.edu.tw/~cjlin/libsvm/

     Check all answers on Stack Overflow

     4/5

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2. en.wikipedia.org › wiki › Support_vector_machineSupport vector machine - Wikipedia

   en.wikipedia.org › wiki › Support_vector_machine

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   In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.

3. www.geeksforgeeks.org › support-vector-machineSupport Vector Machine (SVM) Algorithm - GeeksforGeeks

   www.geeksforgeeks.org › support-vector-machine

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   Jun 10, 2023 · Support Vector Machine (SVM) is a powerful machine learning algorithm used for linear or nonlinear classification, regression, and even outlier detection tasks.

   • Video Duration: 11 min

4. scikit-learn.org › stable › modules1.4. Support Vector Machines — scikit-learn 1.5.0 documentation

   scikit-learn.org › stable › modules

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   Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high dimensional spaces.

5. www.ibm.com › topics › support-vector-machineWhat Is Support Vector Machine? | IBM

   www.ibm.com › topics › support-vector-machine

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   Dec 27, 2023 · A support vector machine (SVM) is a supervised machine learning algorithm that classifies data by finding an optimal line or hyperplane that maximizes the distance between each class in an N-dimensional space.

6. towardsdatascience.com › support-vector-machineSupport Vector Machine — Introduction to Machine Learning ...

   towardsdatascience.com › support-vector-machine

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   Jun 7, 2018 · What is Support Vector Machine? The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.

7. People also ask

   What is a support vector machine in machine learning?

   • In machine learning, support vector machines (SVMs, also support vector networks ) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

   Support Vector Machines for Classification | PPT - SlideShare

   www.slideshare.net/pbpimpale/support-vector-machine-24419322

   See all results for this question

   What is a support vector?

   • Support vectors aredata points that lie closest to the decision surface or the hyperplane. They influence the position and orientation of the decision surface or the hyperplane and are most difficult to classify.

   40+ Data Science Interview Questions and Answers

   www.v7labs.com/blog/data-science-interview-questions-and-answers

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   What is a transductive support vector machine?

   • Transductive support vector machines exploit the geometric (cluster) structure in the feature vectors of the test examples, which makes them a particular kind of semi-supervised learning method. In particular, TSVMs find the labeling of the test examples that maximizes margin jointly on the training and the test data.

   edited by Olivier Chapelle, Bernhard Schölkopf, and

   www.molgen.mpg.de/3659531/MITPress--SemiSupervised-Learning.pdf

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8. machinelearningmastery.com › support-vectorSupport Vector Machines for Machine Learning

   machinelearningmastery.com › support-vector

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   Aug 15, 2020 · Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning.

9. towardsdatascience.com › support-vector-machineSupport Vector Machine Explained. Theory, Implementation, and ...

   towardsdatascience.com › support-vector-machine

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   Jul 30, 2019 · Support Vector Machine (SVM) is probably one of the most popular ML algorithms used by data scientists. SVM is powerful, easy to explain, and generalizes well in many cases. In this article, I’ll explain the rationales behind SVM and show the implementation in Python.