Maha, here are some websites that cover support vector machine learning.

- https://www.analyticsvidhya.com/blog/2021/10/support-vector-machinessvm-a-complete-guide-for-beginners/#:~:text=SVM%20is%20a%20powerful%20supervised,work%20best%20in%20classification%20problems.

- https://www.ibm.com/docs/en/spss-modeler/saas?topic=models-how-svm-works

- https://www.techtarget.com/whatis/definition/support-vector-machine-SVM#:~:text=A%20support%20vector%20machine%20(SVM,which%20are%20labeled%20for%20classification.

I hope that these will help you with your research.

Mary

Support Vector Machines (SVMs) are a type of supervised learning model that are used for classification and regression tasks. However, they are most commonly used in classification problems.

Here's a high-level overview of how an SVM works in a binary classification problem:

Definition of hyperplane: SVMs operate by finding a hyperplane that best separates the data into two classes. In a two-dimensional space, this hyperplane is simply a line. In higher dimensions, it's a plane or a hyper-plane.

Best separation: The "best" hyperplane is the one that maximizes the margin between the two classes. The margin is the distance between the hyperplane and the nearest data point from either class. The nearest data points that touch the margin are known as support vectors. Maximizing the margin ensures that the model generalizes well to unseen data.

Handling non-linear separable data: If the data is not linearly separable, SVM uses a method called the "kernel trick". This method transforms the input space into a higher dimensional space where the data is linearly separable. Once the data is separated, SVM applies a hyperplane to this transformed space.

Regularization: The C parameter in SVMs is a regularisation parameter that controls the trade-off between achieving a low training error and a low testing error that is, the ability to generalize your classifier to unseen data. If overfitting occurs, reducing C helps by encouraging a larger margin, hence a simpler decision function at the cost of training accuracy.

In sum, the SVM algorithm creates a line (or a hyperplane) which separates the data into classes. That's why SVM is also referred to as a "discriminative classifier". It does not model each class separately as in probabilistic models (e.g., Naive Bayes), rather it simply finds a line or curve (in two dimensions) or manifold (in multiple dimensions) that divides the classes from each other.

While SVMs offer high accuracy and can handle high dimensional data well, they can be memory-intensive and difficult to interpret. They're also sensitive to the choice of the kernel parameters and the regularisation parameter, meaning that they can require careful tuning.

A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It is a powerful and versatile algorithm that can handle both linear and non-linear data by mapping input data to a high-dimensional feature space.

The SVM can be effective than ANN specially in handling small-to-medium-sized datasets, especially when the number of features is relatively high. SVMs can handle both linearly separable and non-linearly separable data by utilizing different kernel functions. On the other hand, ANNs, particularly deep learning models, excel in processing large-scale datasets, especially when the data has complex non-linear relationships.

Support Vector Machines (SVMs) are classification algorithms that find the best boundary to separate data points of different classes. They maximize the margin between the boundary and the nearest points of each class. SVMs work for binary and multiclass problems and can be linear or nonlinear. They have good generalizations but can be computationally intensive.

SVM is one of the best Bonary classification algrothim it's classifies with the help of drawing hyper plane.

Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It is primarily used for binary classification, but can also be extended to handle multi-class classification.

The basic idea behind SVM is to find an optimal hyperplane that separates data points of different classes in a high-dimensional feature space. The hyperplane is defined as the decision boundary that maximizes the margin, which is the distance between the hyperplane and the nearest data points of each class. The data points that are closest to the hyperplane are known as support vectors.

Key concepts in SVM include:

A common and effective supervised machine learning approach is called a Support Vector Machine (SVM). It is utilized for classification and regression problems. It works exceptionally well to divide data into two groups in binary classification issues. To handle multi-class classification jobs, SVM may also be expanded. The basic idea of SVM is finding the ideal hyperplane that optimally divides the data points of various classes in the feature space. The hyperplane is a decision boundary that optimizes the margin between the two classes. The margin separates each class's closest data points and the hyperplane. SVM seeks to increase this margin to improve generalization and resilience on untested data.

A quick explanation of how SVM works for binary classification is given below:

1. Data Representation

2. Finding the Optimal Hyperplane

3. Margin and Support Vectors

4. Soft Margin

5. Kernel Trick

6. Training and Prediction

Simply it's is a classification algorithm for any input data, using hyperplane lines are drawn between the data points and setting threshold 0 to 1 range ....