# 서포트 벡터 머신

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## 노트

### 위키데이터

- ID : Q282453

### 말뭉치

- A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems.
^{[1]} - The basics of Support Vector Machines and how it works are best understood with a simple example.
^{[1]} - A support vector machine takes these data points and outputs the hyperplane (which in two dimensions it’s simply a line) that best separates the tags.
^{[1]} - For SVM, it’s the one that maximizes the margins from both tags.
^{[1]} - A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane.
^{[2]} - That’s what SVM does.
^{[2]} - These are tuning parameters in SVM classifier.
^{[2]} - The learning of the hyperplane in linear SVM is done by transforming the problem using some linear algebra.
^{[2]} - The dataset we will be using to implement our SVM algorithm is the Iris dataset.
^{[3]} - There is another simple way to implement the SVM algorithm.
^{[3]} - We can use the Scikit learn library and just call the related functions to implement the SVM model.
^{[3]} - What makes SVM different from other classification algorithms is its outstanding generalization performance.
^{[4]} - Actually, SVM is one of the few machine learning algorithms to address the generalization problem (i.e., how well a derived model will perform on unseen data).
^{[4]} - How to implement SVM in Python and R?
^{[5]} - How to tune Parameters of SVM?
^{[5]} - “Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges.
^{[5]} - In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.
^{[5]} - The support vector machines in scikit-learn support both dense ( numpy.ndarray and convertible to that by numpy.asarray ) and sparse (any scipy.sparse ) sample vectors as input.
^{[6]} - However, to use an SVM to make predictions for sparse data, it must have been fit on such data.
^{[6]} - Note that the LinearSVC also implements an alternative multi-class strategy, the so-called multi-class SVM formulated by Crammer and Singer , by using the option multi_class='crammer_singer' .
^{[6]} - In the binary case, the probabilities are calibrated using Platt scaling : logistic regression on the SVM’s scores, fit by an additional cross-validation on the training data.
^{[6]} - An SVM maps training examples to points in space so as to maximise the width of the gap between the two categories.
^{[7]} - Some methods for shallow semantic parsing are based on support vector machines.
^{[7]} - This is also true for image segmentation systems, including those using a modified version SVM that uses the privileged approach as suggested by Vapnik.
^{[7]} - Classification of satellite data like SAR data using supervised SVM.
^{[7]} - Support Vector Machine has become an extremely popular algorithm.
^{[8]} - SVM is a supervised machine learning algorithm which can be used for classification or regression problems.
^{[8]} - In this post I'll focus on using SVM for classification.
^{[8]} - In particular I'll be focusing on non-linear SVM, or SVM using a non-linear kernel.
^{[8]} - A support vector machine (SVM) model is a supervised learning algorithm that is used to classify binary and categorical response data.
^{[9]} - SVM models classify data by optimizing a hyperplane that separates the classes.
^{[9]} - The maximization in SVM algorithms is performed by solving a quadratic programming problem.
^{[9]} - In JMP Pro, the algorithm used by the SVM platform is based on the Sequential Minimal Optimization (SMO) algorithm introduced by John Platt in 1998.
^{[9]} - The standard SVM takes a set of input data and predicts, for each given input, which of the two possible classes comprises the input, making the SVM a non-probabilistic binary linear classifier.
^{[10]} - Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other.
^{[10]} - An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible.
^{[10]} - This learner uses the Java implementation of the support vector machine mySVM by Stefan Rueping.
^{[10]} - next → ← prev Support Vector Machine Algorithm Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems.
^{[11]} - SVM chooses the extreme points/vectors that help in creating the hyperplane.
^{[11]} - These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
^{[11]} - Example: SVM can be understood with the example that we have used in the KNN classifier.
^{[11]} - Then, the operation of the SVM algorithm is based on finding the hyperplane that gives the largest minimum distance to the training examples.
^{[12]} - Twice, this distance receives the important name of margin within SVM's theory.
^{[12]} - However, SVMs can be used in a wide variety of problems (e.g. problems with non-linearly separable data, a SVM using a kernel function to raise the dimensionality of the examples, etc).
^{[12]} - As a consequence of this, we have to define some parameters before training the SVM.
^{[12]} - In this study, we propose a multivariate linear support vector machine (SVM) model to solve this challenging binary classification problem.
^{[13]} - Moreover, the number of features found by linear SVM was also fewer than that of logistic regression (five versus six), which makes it easier to be interpreted by chemists.
^{[13]} - Working set selection using second order information for training SVM.
^{[14]} - Our goal is to help users from other fields to easily use SVM as a tool.
^{[14]} - Support vector machines (SVMs) are a well-researched class of supervised learning methods.
^{[15]} - This SVM model is a supervised learning model that requires labeled data.
^{[15]} - Add the Two-Class Support Vector Machine module to your pipeline.
^{[15]} - You can use a support vector machine (SVM) when your data has exactly two classes.
^{[16]} - An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of the other class.
^{[16]} - The best hyperplane for an SVM means the one with the largest margin between the two classes.
^{[16]} - One strategy to this end is to compute a basis function centered at every point in the dataset, and let the SVM algorithm sift through the results.
^{[17]} - This kernel trick is built into the SVM, and is one of the reasons the method is so powerful.
^{[17]} - In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identifyspp.
^{[18]} - SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature.
^{[18]} - Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers.
^{[18]} - See Support Vector Machine Background for details.
^{[19]} - Note: SVM classification can take several hours to complete with training data that uses large regions of interest (ROIs).
^{[19]} - Display the input image you will use for SVM classification, along with the ROI file.
^{[19]} - From the Toolbox, select Classification > Supervised Classification > Support Vector Machine Classification.
^{[19]} - The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results.
^{[20]} - Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical regularization procedure.
^{[20]} - Support vector machines (SVMs) are powerful yet flexible supervised machine learning algorithms which are used both for classification and regression.
^{[21]} - The hyperplane will be generated in an iterative manner by SVM so that the error can be minimized.
^{[21]} - In practice, SVM algorithm is implemented with kernel that transforms an input data space into the required form.
^{[21]} - SVM uses a technique called the kernel trick in which kernel takes a low dimensional input space and transforms it into a higher dimensional space.
^{[21]} - Support vector machines are a set of supervised learning methods used for classification, regression, and outliers detection.
^{[22]} - A simple linear SVM classifier works by making a straight line between two classes.
^{[22]} - What makes the linear SVM algorithm better than some of the other algorithms, like k-nearest neighbors, is that it chooses the best line to classify your data points.
^{[22]} - We'll do an example with a linear SVM and a non-linear SVM.
^{[22]} - The idea behind the basic support vector machine (SVM) is similar to LDA - find a hyperplane separating the classes.
^{[23]} - Using a SVM, the focus is on separating the closest points in different classes.
^{[23]} - However, like LDA, SVM requires groups that can be separated by planes.
^{[23]} - The kernel trick uses the fact that for both LDA and SVM, only the dot product of the data vectors \(x_i'x_j\) are used in the computations.
^{[23]} - Support vector machines operate by drawing decision boundaries between data points, aiming for the decision boundary that best separates the data points into classes (or is the most generalizable).
^{[24]} - You can think of a support vector machine as creating “roads” throughout a city, separating the city into districts on either side of the road.
^{[24]} - So how does a support vector machine determine the best separating hyperplane/decision boundary?
^{[24]} - However, SVM classifiers can also be used for non-binary classification tasks.
^{[24]} - A support vector machine (SVM) is a type of supervised machine learning classification algorithm.
^{[25]} - In this article we'll see what support vector machines algorithms are, the brief theory behind support vector machine and their implementation in Python's Scikit-Learn library.
^{[25]} - This is a binary classification problem and we will use SVM algorithm to solve this problem.
^{[25]} - Now is the time to train our SVM on the training data.
^{[25]} - Box plots report the prediction accuracy of (a) SVM and (b) SVR calculations over all activity classes and 10 independent trials per class.
^{[26]} - For SVM calculations, the F1 score, AUC, and recall of active compounds among the top 1% of the ranked test set are reported.
^{[26]} - Figure 1 summarizes the performance of our SVM and SVR models on the 15 activity classes using different figures of merit appropriate for assessing classification and regression calculations.
^{[26]} - Therefore, features were randomly removed from SVM models or in the order of decreasing feature weights, and classification calculations were repeated.
^{[26]} - This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM).
^{[27]} - A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost.
^{[27]} - The LS-SVM involves finding a separating hyperplane of maximal margin and minimizing the empirical risk via a Least Squares loss function.
^{[27]} - Thus the LS-SVM successfully sidesteps the quadratic programming (QP) required for the training of the standard SVM.
^{[27]}

### 소스

- ↑
^{1.0}^{1.1}^{1.2}^{1.3}An Introduction to Support Vector Machines (SVM) - ↑
^{2.0}^{2.1}^{2.2}^{2.3}Chapter 2 : SVM (Support Vector Machine) — Theory - ↑
^{3.0}^{3.1}^{3.2}Support Vector Machine — Introduction to Machine Learning Algorithms - ↑
^{4.0}^{4.1}Support Vector Machine - an overview - ↑
^{5.0}^{5.1}^{5.2}^{5.3}Support Vector Machine Algorithm in Machine Learning - ↑
^{6.0}^{6.1}^{6.2}^{6.3}1.4. Support Vector Machines — scikit-learn 0.23.2 documentation - ↑
^{7.0}^{7.1}^{7.2}^{7.3}Support vector machine - ↑
^{8.0}^{8.1}^{8.2}^{8.3}What is a Support Vector Machine, and Why Would I Use it? - ↑
^{9.0}^{9.1}^{9.2}^{9.3}Overview of Support Vector Machines - ↑
^{10.0}^{10.1}^{10.2}^{10.3}RapidMiner Documentation - ↑
^{11.0}^{11.1}^{11.2}^{11.3}Support Vector Machine (SVM) Algorithm - ↑
^{12.0}^{12.1}^{12.2}^{12.3}OpenCV: Introduction to Support Vector Machines - ↑
^{13.0}^{13.1}Linear support vector machine to classify the vibrational modes for complex chemical systems - ↑
^{14.0}^{14.1}LIBSVM -- A Library for Support Vector Machines - ↑
^{15.0}^{15.1}^{15.2}Two-Class Support Vector Machine: Module Reference - Azure Machine Learning - ↑
^{16.0}^{16.1}^{16.2}Support Vector Machines for Binary Classification - ↑
^{17.0}^{17.1}In-Depth: Support Vector Machines - ↑
^{18.0}^{18.1}^{18.2}Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data - ↑
^{19.0}^{19.1}^{19.2}^{19.3}Support Vector Machine - ↑
^{20.0}^{20.1}Blanchard , Bousquet , Massart : Statistical performance of support vector machines - ↑
^{21.0}^{21.1}^{21.2}^{21.3}Support Vector Machine(SVM) - ↑
^{22.0}^{22.1}^{22.2}^{22.3}SVM Machine Learning Tutorial – What is the Support Vector Machine Algorithm, Explained with Code Examples - ↑
^{23.0}^{23.1}^{23.2}^{23.3}14.4 - Support Vector Machine - ↑
^{24.0}^{24.1}^{24.2}^{24.3}What are Support Vector Machines? - ↑
^{25.0}^{25.1}^{25.2}^{25.3}Implementing SVM and Kernel SVM with Python's Scikit-Learn - ↑
^{26.0}^{26.1}^{26.2}^{26.3}Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction - ↑
^{27.0}^{27.1}^{27.2}^{27.3}A Novel Sparse Least Squares Support Vector Machines

## 메타데이터

### 위키데이터

- ID : Q282453