# What is Support vector machines

##### Understanding Support Vector Machines (SVMs) for Machine Learning

Support Vector Machines (SVMs) is a popular Machine Learning algorithm that is primarily used for classification and regression purposes. This algorithm is widely used in various applications, including image classification, text classification, pattern recognition, and bioinformatics. The primary objective of SVM is to find the best boundary that separates the different classes while maximizing the margin.

##### The Basic Idea Behind Support Vector Machines

The basic idea behind the Support Vector Machines is to find a decision boundary that best separates the classes in the given data set. The decision boundary is defined mathematically as a hyperplane, which is a higher dimensional plane that separates different classes. A hyperplane is essentially a boundary that separates a dataset into two classes, and we try to optimize the hyperplane parameters such that it can classify the data points accurately.

##### Linear Support Vector Machines

The primary objective of Linear Support Vector Machines is to find the best hyperplane that separates the data points and maximizes the margin. A margin is the distance between the hyperplane and the nearest data point from each class. The margin is the closest distance between the hyperplane and any data point from the training set, and it is computationally difficult to find the optimal margin. SVM solves this problem through a constrained optimization approach, where the objective function is to minimize the classification error while maximizing the margin.

##### Non-linear Support Vector Machines

In many real-world applications, the data is usually not linearly separable. The decision boundary for such problems can be curved or nonlinear. Nonlinear Support Vector Machines can be used to solve such problems, and it relies on a kernel function to transform the data points into a higher dimensional space where the data can be linearly separated. The transformed data is then classified with a linear hyperplane in the higher dimensional space.

##### Kernel Functions in Support Vector Machines

The Kernel Function is a critical component of the Non-linear Support Vector Machines algorithm as it helps transform the data into a higher dimensional space where the data can be linearly separated. SVM uses several kernel functions such as linear, polynomial, Gaussian RBF, sigmoid, etc. to transform the data into higher dimensional spaces.

##### Advantages of Support Vector Machines
• SVM is highly effective in high-dimensional spaces.
• It is versatile and can be used for both classification and regression.
• The kernel function provides a flexible framework for transforming the data points into higher dimensional spaces where the data can be linearly separable.
• It provides robust and accurate results even when the data points are noisy or have outliers.
• SVM can handle large datasets efficiently and can also be used incrementally.
##### Disadvantages of Support Vector Machines
• SVM is computationally intensive, and it requires a lot of time and resources to train when the data set is large.
• The choice of kernel function and its parameters can significantly impact the performance of the algorithm.
• Interpretation of results can be challenging due to the complexity of the algorithm.
##### Applications of Support Vector Machines

SVM has found various applications across a wide range of domains, some of which are:

• Image classification
• Text classification
• Natural Language Processing (NLP)
• Speech Recognition
• Bioinformatics
##### Conclusion

Support Vector Machines is a powerful algorithm that has found applications in various fields such as image classification, text classification, and bioinformatics. It is highly effective in high-dimensional spaces and can handle noisy data with outliers. Although SVM is computationally intensive, it provides robust and accurate results. Nonlinear Support Vector Machines, which rely on kernel functions, can transform the data points into higher dimensional spaces where the data can be linearly separable. Overall, SVM is an exciting algorithm, and it continues to be an area of active research.