Support vector classifier. Support vector classifiers — Transparent ML Intro.

. Create a support vector classifier: → svc=svm May 9, 2020 · Logistic Regression (LR) is a probabilistic classification model using the sigmoid function, whereas Support Vector Classifiers (SVC) are a more geometric approach that maximise the margins to each class. Before getting more into the theory of how SVM works, we can build our first baseline model with the data, and Scikit-Learn's Support Vector Classifier or SVC class. I am having trouble plotting the ROC & AUC . The following steps will be covered for training the model using SVM while using Python code: Load the data. It is used for smaller dataset as it takes too long to process. 8. Measure the model performance. So we see in the example below we have two classes denoted by violet triangles and orange crosses. Support V ector Machines for. Plot the support vectors in LinearSVC. Understand the terminology, mathematical formulation, and kernel tricks of SVM. This example shows how different kernels in a SVC (Support Vector Classifier) influence the classification boundaries in a binary, two-dimensional classification problem. The SVM algorithm finds a hyperplane decision boundary that best splits the examples into two classes. 5. Support Vector Machine (SVM) SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. May 21, 2020 · Based on an extensive literature analysis, one-class support vector classifiers (OCSVCs) are found suitable for anomaly and novelty detection in numerous applications such as document classification [12], disease diagnosis [13], [14], fraud detection [15], [16], intrusion detection [17], [18] and novelty detection [19]. It classifies data into finer and finer categories: from “tree trunk,” to “branches,” to “leaves. Support vector classifiers — Transparent ML Intro. When a computer processes an image, it perceives it as a two-dimensional array of pixels. On my side I’ve been trying to read articles and check but unsuccessful until. The support vector classifier aims to create a decision line that would class a new observation as a Jan 1, 2023 · To hide the support vectors, Rubinstein et al. The decision function is fully specified by a (usually very small) subset of training samples, the support vectors. Finally, that’s it. Sep 30, 2020 · Development of optimal classifiers for multi-class problems. The aforementioned issue for support vector classifiers can be resolved by using a maximum margin hyperplane – the lines that produce the greatest gap between A support vector machine (SVM) is a type of supervised learning algorithm used in machine learning to solve classification and regression tasks; SVMs are particularly good at solving binary classification problems, which require classifying the elements of a data set into two groups. A separating hyperplane is a hyperplane that separates two classes We then show the effectiveness of this classifier by evaluating performance in terms of standard precision, recall and f-measure for a collection of test documents. Scaling the regularization parameter for SVCs. svm import SVC svc = SVC() svc. The goal of a classifier in our example below is to find a line or (n-1) dimension hyper-plane that separates the two classes present in the n-dimensional space. For classification, this article examined the top six machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, Support Vector Machines, K-Nearest Neighbors, and Gradient Boosting. The split is made soft through the use of a margin that allows some points to be misclassified. fit(X_train, y_train) Step 6: Predicting the Test set results. Concretely, C -support vector machine ( C -SVM) uses the hinge loss to maximize In this article, the classical support vector machine (SVM) classifiers are generalized by the non-Euclidean margins. fit_status_ int. Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. Si bien originariamente se desarrolló como un método de clasificación binaria, su aplicación se ha extendido a problemas de clasificación múltiple y This motivates the concept of a support vector classifier (SVC). 서포트 벡터 머신 ( support vector machine, SVM [1] [2] )은 기계 학습 의 분야 중 하나로 패턴 인식, 자료 분석을 위한 지도 학습 모델이며, 주로 분류 와 회귀 분석 을 위해 사용한다. This example shows how to use the ClassificationSVM Predict block for label prediction in Simulink®. In this section, you’ll learn how to use Scikit-Learn in Python to build your own support vector machine model. Typical examples include C, kernel and gamma for Support Vector Classifier, alpha for Lasso, etc. classifier = SVC (kernel='linear', random_state=0) classifier. SVCs are widely used in a variety of applications, including image classification, text classification Support Vector Machine (SVM) was first heard in 1992, introduced by Boser, Guyon, and Vapnik in COLT-92. The support vector classifier maximizes a soft margin. Many studies have considered model selection methods. The maximal margin classifier is simple, but it cannot be applied to the majority of datasets, since the classes must be separated by a linear boundary. Nevertheless, the model then, on average, performs better on test data. Total running time of the script: (0 minutes 0. For simplicity, I’ll focus on binary classification problems in this article. supervised learning algorithm. Apr 30, 2019 · Support Vector Machine (SVM) is one of the most popular classification techniques which aims to minimize the number of misclassification errors directly. Mar 3, 2021 · “Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges. Towards , the end of my program, I have the following code. It is well suited for segmented raster input but can also handle standard imagery. predict(X_test) support vector machine common hyperparameters: c, kernel, gamma. , they are robust to the high variable-to-sample ratio and large number of variables, they can learn efficiently complex classification functions, and they employ powerful regularization principles to avoid Jan 15, 2019 · In machine learning, Support Vector Machine (SVM) is a non-probabilistic, linear, binary classifier used for classifying data by learning a hyperplane separating the data. dot product. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. It was initially introduced to address binary classification problems but with time, it was extended to include regression Figure 15. 195 seconds) Feb 7, 2022 · Kernel Function is a method used to take data as input and transform it into the required form of processing data. fit(X_train, y_train) y_pred = svc. 4. See full list on towardsdatascience. This becomes a Quadratic programming problem that is easy to solve by standard methods. Jan 30, 2023 · Support vector regression (SVR) is a type of support vector machine (SVM) that is used for regression tasks. There are many accessible resources to understand the basics of how Support Vector Machines (SVMs) work, however, in almost all the real-world applications (where the data is linearly Substituting these values back in (and simplifying), we obtain: (Dual) Sums over all training examples. The separation line found in this case is called support vector classifier(SVC). This chapter covers details of the support vector machine Coefficients of the support vector in the decision function. y_pred = classifier. 0031259768677711786, random_state=None, shrinking=True, tol=0. To tell the SVM story, we'll need to rst talk about margins and the idea of sepa. In this step, the classifier. In SVM, we Dec 6, 2023 · Support Vector Classifiers (SVCs) are a type of machine learning algorithm that can be used for classification tasks. Intuitively, a decision boundarydrawn in the middle ofthe void between data items of the two classes seems betterthan one Support Vector Machine (SVM)—The SVM classifier provides a powerful, supervised classification method that can process a segmented raster input or a standard image. It is possible and recommended to search the hyper-parameter space for the best cross validation score. Hyper-parameters like C or Gamma control how wiggling the SVM decision boundary could be. predict(X_test) y_pred Sep 27, 2023 · Support Vector Machine (SVM) is one of the supervised machine learning algorithms that can be used for different purposes: classification, regression, and even anomaly detection. Mar 27, 2023 · Support vector machine (SVM) Python example. Perform feature scaling. A note about the Soft margin and May 3, 2017 · A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. For two-class, separable training data sets, such as the one inFigure 14. Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vectors. Each classifier is trained on a single binary dataset, and the final class is predicted by a majority vote between all the classifiers. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane that categorizes new examples. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression [1]. The problem of multi-classification for SVM, does not present an easy solution. For the time being, we will use a linear kernel and set the C parameter to a very large number (we'll discuss the meaning of these in more depth momentarily). Jan 7, 2019 · By combining the soft margin (tolerance of misclassifications) and kernel trick together, Support Vector Machine is able to structure the decision boundary for linear non-separable cases. . SVM is also known as the support vector network. Consider an example where we have cats and dogs together. Here, we are using linear kernel to fit SVM as follows −. svm import SVC # "Support vector classifier". svm library. Feb 2, 2023 · Introduction to SVMs: In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Dec 14, 2020 · Naive Bayes Classifier; K-Nearest Neighbors; Support Vector Machines; Artificial Neural Networks; Decision Tree. svm module to create an instance of the SVM classifier. As we alluded to above, one of the problems with MMC is that they can be extremely sensitive to the addition of new training observations. scalars. from sklearn. Jul 4, 2024 · Learn how SVM finds the optimal hyperplane to separate data points in different classes using linear or nonlinear classification, regression, and outlier detection. Classification methods from machine learning have transformed difficult data analysis. Science is the systematic classification of experience. Support Vector Machines were originally designed to solve binary classification problems [122]. Andrew Ng. Instantiate an SVC classifier. Aug 8, 2023 · Abstract and Figures. 001, verbose=False) output. As is well-known, selecting one winning model over others can entail considerable instability in predictive Sep 23, 2021 · Support Vector Classifier Simply Explained [With Code] Support Vector Machine (SVM) is a supervised machine learning algorithm that has become immensely popular in solving classification as well as regression problems. Continuing the analogy from maximum margin classifier, only the points that are on the margin or between the margin will participate in determining the separating hyperplane. Where ϵ i ≥ 0 and ∑ i = 1 n ϵ i ≤ C. An important question for SVC is the selection of covariates (or features) for the model. Feb 25, 2023 · A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. Below is the code for it: from sklearn. SVMs are among the best (and many believe is indeed the best) \o -the-shelf". It tries to find a function that best predicts the continuous output value for a given input value. SVC aims to draw a straight line between two classes such that the gap between the two classes is as wide as possible. Aug 30, 2020 · from sklearn. This hyperplane building procedure varies and is the main task of an SVM classifier. For standard image inputs, the tool accepts multiband imagery with any bit depth, and it will perform the SVM classification on a Dec 20, 2023 · The first few lines create a pipeline that scales the data and uses a support vector classifier SVC with a polynomial kernel, degree of 10, coefficient of 100, and C=5. In Depth: Support Vector Machines. Let’s begin by importing the required libraries for this Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data. 1:The support vectors are the 5 points right up against the margin of the classifier. SVR can use both linear and non-linear kernels. Expand. This section introduces the concept of an optimal separating hyperplane, the maximal margin classifier, and the support vector classifier, laying the groundwork for the support vector machine. 2. Noted that Support Vector Classifier works for only separation of 2-class problem while Support Vector Machine (SVM) is able to cope with more complex non-linear problem with more than 2-class classification. In other words, given labeled training data ( supervised learning ), the algorithm Apr 26, 2020 · Noted: Support Vector Classifier only supports for 2-classes classification if the boundary is linear to separate the observations. Fitting a support vector machine¶ Let's see the result of an actual fit to this data: we will use Scikit-Learn's support vector classifier to train an SVM model on this data. We begin with the standard imports: Jul 31, 2019 · Support Vector Machine (SVM) is probably one of the most popular ML algorithms used by data scientists. svm import SVC # "Support vector classifier" model = SVC(kernel='linear', C=1E10) model. Apr 27, 2015 · Chapter 3. Next, we will use Scikit-Learn’s support vector classifier to train an SVM model on this data. The size of the array corresponds to the Aug 28, 2020 · Ridge Classifier; K-Nearest Neighbors (KNN) Support Vector Machine (SVM) Bagged Decision Trees (Bagging) Random Forest; Stochastic Gradient Boosting; We will consider these algorithms in the context of their scikit-learn implementation (Python); nevertheless, you can use the same hyperparameter suggestions with other platforms, such as Weka and R. The SVM classifier is a powerful supervised classification method. They were very famous around the time they were created, during the 1990s 2 days ago · A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In this article, I’ll explain the rationales behind SVM and show the implementation in Python. The optimization problem can be modified as. In a nutshell, the main focus of an SVM algorithm is to find the decision boundary that can separate different classes of data distinctively. The principle behind an SVM classifier (Support Vector Machine) algorithm is to build a hyperplane separating data for different classes. Output. Dual formulation only depends on dot-products of the features! First, we introduce a feature mapping: . 2) Finding the SVC. Logistic regression is a probabilistic binary linear classifier, meaning it calculates the probability that a data point Aug 8, 2023 · Support vector classification (SVC) is a well-known statistical technique for classification problems in machine learning and other fields. They were designed to address a longstanding problem with logistic regression, another machine learning technique used to classify data. We have included a function for this in the ISLP package (inspired by a similar example in the sklearn docs). Jul 29, 2019 · The support vector machine is a generalization of a classifier called maximal margin classifier. SVM is a computation-friendly modelling technique that is widely used Jan 8, 2013 · A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. Mar 16, 2022 · Support Vector Machines for Machine Learning; A Tutorial on Support Vector Machines for Pattern Recognition by Christopher J. Support vector classifiers. predict() function is used to predict the values for the Test set and the values are stored to the variable y_pred. El método de clasificación-regresión Máquinas de Vector Soporte (Vector Support Machines, SVMs) fue desarrollado en la década de los 90, dentro de campo de la ciencia computacional. Support Vector Classifiers. svm import SVC classifier = SVC(kernel = 'rbf', random_state = 0) classifier. 2. Aug 21, 2020 · The Support Vector Machine algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. C. Burges; Summary. The Support Vector Machine (SVM) is a linear classifier that can be viewed as an extension of the Perceptron developed by Rosenblatt in 1958. Part VSupport Vector MachinesThis set of notes presents the Support Vector Mac. Vapnik and his colleagues, and they published this work in a paper titled "Support Jul 7, 2020 · SVM: Support Vector Machine is a supervised classification algorithm where we draw a line between two different categories to differentiate between them. Jun 4, 2020 · In summary, SVMs pick the decision boundary that maximizes the distance to the support vectors. The εi is the slack corresponding to i t h observation and C is a regularization parameter set by the user. —George Henry Lewes. The aim of a support vector machine algorithm is to find the May 22, 2024 · Introduction. In this article, we will learn about the mathematics involved behind the SVM for a classification problem, how it classifies the classes, and how it Mar 26, 2024 · Conclusion. A decision tree is a supervised machine learning classification algorithm used to build models like the structure of a tree. “Kernel” is used due to a set of mathematical functions used in Support Vector Machine providing the window to manipulate the data. Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks, but generally, they work best in classification problems. In this chapter, we will explore the intuition behind SVMs and their use in classification problems. In order to create support vector machine classifiers in sklearn, we can use the SVC class as part of the svm module. In this tutorial, you discovered how to use the method of Lagrange multipliers to solve the problem of maximizing the margin via a quadratic programming problem with inequality constraints. SVMs were developed in the 1990s by Vladimir N. The decision boundary is drawn in a way that the distance to support vectors are maximized. com Support Vector Machine (SVM) SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. #. This example demonstrates how to obtain the support vectors in LinearSVC. They are similar in that they both can divide the feature space with a decision boundary. An important question for SVC is Dec 8, 2022 · This raises the question of how a support vector classifier machine should handle these conditions in order to model for the specific classification problems that are being presented. One approach for using binary classification algorithms for multi-classification problems is to split the multi-class Jun 25, 2024 · One classifier we encounter while learning about machine learning is the Support Vector Machine (SVM). This algorithm is one of the most popular classification algorithms used in machine learning. It is a classification method commonly used in the research community. Classification. May 31, 2017 · Support Vector Classifiers. Setting: We define a linear classifier: h(x) = sign(wTx + b) h ( x Jul 2, 2023 · Implementing SVM with Scikit-Learn. All such points are called support vectors, since they are literally supporting the determination of the boundary. y i ( θ 0 + θ 1 x 1 i + θ 2 x 2 i + ⋯ + θ n x n i) ≥ 1 – ϵ i for every observation. Jan 16, 2024 · The support vector machine (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. So, Kernel Function generally transforms the training set of data so that a non-linear decision Generates an Esri classifier definition (. They belong to a family of generalized linear classifiers. Oct 20, 2018 · Support vector machines so called as SVM is a supervised learning algorithm which can be used for classification and regression problems as support vector classification (SVC) and support vector regression (SVR). Jan 19, 2017 · For Implementing a support vector machine, we can use the caret or e1071 package etc. Create training and test split. SVM is powerful, easy to explain, and generalizes well in many cases. Algorithms such as the Perceptron, Logistic Regression, and Support Vector Machines were designed for binary classification and do not natively support classification tasks with more than two classes. In Fig 8 it can be seen that there exists a MMH perfectly separating the two classes. Specially, the SVM classifiers by the $ \\infty $-norm margins can be solved by the 1 Mar 10, 2020 · 0. For SVC classification, we are interested in a risk minimization for the equation: C ∑ i = 1, n L ( f ( x i), y i) + Ω ( w) where. fig,ax=subplots(figsize=(8,8))plot_svm(X,y,svm_linear,ax=ax) The decision boundary between the two classes is linear Neural Network Classifier & Regressor; Training a Quantum Model on a Real Dataset; Quantum Kernel Machine Learning; PyTorch qGAN Implementation; Torch Connector and Hybrid QNNs; Pegasos Quantum Support Vector Classifier; Quantum Kernel Training for Machine Learning Applications; Saving, Loading Qiskit Machine Learning Models and Continuous Training Aug 23, 2023 · Here’s an example of SVM classifier Python code implementation in Python along with an explanation of each line of code: Explanation of each line of the svm classifier python code: Line 1: Import the necessary libraries. We need not build SVCs from the roots. We want our model to differentiate between cats and dogs. The Perceptron guaranteed that you find a hyperplane if it exists. g. In other words, given labeled training data ( supervised learning ), the algorithm outputs an optimal hyperplane which categorizes new examples. They are based on the idea of finding a hyperplane that separates the data into two classes with the largest possible margin. However, the nondifferentiability of the SVM hinge loss function can lead to computational difficulties in high-dimensional settings. Jun 12, 2023 · A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In general, for N labels, we will have Nx(N-1)/2 classifiers. We import the SVC class from the sklearn. Classifying a non-linearly separable dataset using a SVM – a linear classifier: As mentioned above SVM is a linear classifier which learns an (n – 1)-dimensional Aug 8, 2023 · Model averaging for support vector classifier by cross-validation. Apr 2, 2021 · The way out is to misclassify particular points. The fundamental idea of SVM is to find a hyperplane in the feature space that divides different categories of observations to the largest separation. Consider Figs 8 and 9. Aug 1, 2023 · Prerequisite: Support Vector Machines Definition of a hyperplane and SVM classifier: For a linearly separable dataset having n features (thereby needing n dimensions for representation), a hyperplane is basically an (n - 1) dimensional subspace used for separating the dataset into two sets, each set containing data points belonging to a different c Nov 28, 2021 · Support Vector Machine was developed in 1990 and has since gained popularity as one of the best ‘out of box’ classifiers. That is why the support vector classifier was introduced as an extension of the maximal margin Feb 25, 2022 · Support Vector Machines in Python’s Scikit-Learn. In ot Aug 15, 2020 · What does support vector machine (SVM) mean in layman’s terms? Please explain Support Vector Machines (SVM) like I am a 5 year old; Summary. fit(X, y) The output is as follows − Support Vector Machine is useful in finding the separating Hyperplane ,Finding a hyperplane can be useful to classify the data correctly between different groups. [32] first proposed a weight vector perturbation for reproducing kernel Hilbert spaces (RKHS) with translation-invariant kernels. In this article, we will focus on using SVMs for image classification. Doing so slumps the classification accuracy on training data. The most important question that arises while using SVM is how to decide the right hyperplane. The algorithm first solves the dual problem of the SVM and perturbs the primal weight vector to guarantee DP. We first extend the linear models of the SVM classifiers by the non-Euclidean margins including the theorems and algorithms of the SVM classifiers by the hard margins and the soft margins. 0 if correctly fitted, 1 otherwise (will raise warning) intercept_ ndarray of shape (1,) Constants in decision function. SVM is a powerful supervised algorithm that works best on smaller datasets but on complex ones. The SVM finds the maximum margin separating hyperplane. Fit the model. For all the others, λ j = 0 and the inquality y j ( β C-Support Vector Classification. You learned about: The Maximal-Margin Classifier that provides a simple theoretical model for understanding SVM. This work implements support vector machine training algorithm to classify research papers because it is fast classification, fast learning and more accurate technique. output. Our model will receive the wavelet coefficients and try to classify them based on the class. It is less susceptible to noise, correlated bands, and an unbalanced number or size of training sites within each class. Number of features seen during fit. If the decision boundary is too close to the support vectors then, it will be sensitive to noise and not generalize well. We propose a Variational Quantum The support vector classifier with two features can be visualized by plotting values of its decision function . In this post you discovered the Support Vector Machine Algorithm for machine learning. Perform binary classification via SVM using separating hyperplanes and kernel transformations. ecd) file using the Support Vector Machine (SVM) classification definition. SVCs aim to find a hyperplane that effectively separates the classes in their training data by maximizing the margin between the outermost data points of each class. May 21, 2024 · Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification and regression tasks. Support vector classification (SVC) is a well-known statistical technique for classification problems in machine learning and other fields. A model weighting scheme is proposed and the theoretical underpinning for the proposed method is provided, which yields a model average estimator that achieves the smallest hinge risk among all feasible combinations asymptotically. If we have a 3-class classification, we will have three pairs of labels, thus three classifiers, as shown below. max_iter=-1, nu=0. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. Explore the differences between support vector machine and support vector classifier on Zhihu's column, where you can write freely and express yourself. video II. To overcome this problem, we rely on the Bernstein polynomial and propose a new smoothed version of the SVM hinge loss called the Nov 16, 2023 · SVM – Maximal Margin Classifier – First Song: For MMC – Support Vector, Hyperplane, and Margin (Romance Song!) Support Vector Classifier (SVC)(Second Song): Limitation of SVC (Sudden Twist in the movie): Support Vector Machine (SVM) – (Interval block): Polynomial SVM Kernel: (#1 Fight Scene!) Radial Basis Function Kernel: (#2 Fight Scene!) Apr 27, 2021 · Not all classification predictive models support multi-class classification. 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. A linear kernel is a simple dot product between two input vectors, while a non-linear The SVM classifier is a supervised classification method. Feb 22, 2022 · Similar to decision tree and random forest, support vector machine can be used in both classification and regression, SVC (support vector classifier) is for classification problem. Next, replace the dot product with an equivalent kernel function: CS229 Lecture notes. fit (x_train, y_train) In the above code, we have used kernel='linear', as here we are creating SVM for linearly The original support vector machines ( SVMs) were invented by Vladimir Vapnik in 1963. 8(page ), there are lots of possible linear separators. ” To create the SVM classifier, we will import SVC class from Sklearn. The following example illustrates the effect of scaling the regularization parameter when using Support Vector Machines for classification . SVM has a nature of Convex Optimization which is very helpful as we are assured of optimality in results So the answer would be global minimum instead of a local minimum. ine (SVM) learning al-gorithm. SVM or support vector machine is the classifier that maximizes the margin. Aug 1, 2017 · Support vector machine (SVM) [1], [2] is a powerful machine learning tool for classification and regression tasks. fit method then trains Jul 22, 2008 · Support vector machine classifiers Several theoretical reasons explain the superior empirical performance of SVMs in microarray data: e. Usage. 두 카테고리 중 어느 하나에 속한 데이터의 집합이 주어졌을 때, SVM 알고리즘은 주어진 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. The implementation is based on libsvm. The . n_features_in_ int. Sep 24, 2019 · Predicting qualitative responses in machine learning is called classification. Research on Improving Accuracy of MWD Based on Support Vector Classifier and K-Proximity Method Abstract: With the development of horizontal Wells and extended reach Wells in the field of oil and gas exploitation, it is of great significance to the development of MWD which can measure the drilling trajectory in real time. ec jg dh cd pt oj ta dx ym hv