If we talk about Optimality:

In the Greedy method approach, there is no guarantee of getting the optimal solution, Generally, it gives the optimal local solution.

while

In the dynamic approach, there is a guarantee of getting an optimal solution as it considers all the cases as long to get optimal. It gives optimal global solutions.

For example, let’s say that you have to get from point A to point B as fast as possible, in a given city, during rush hour. A dynamic programming algorithm will look into the entire traffic report, looking into all possible combinations of roads you might take, and will only then tell you which way is the fastest. Of course, you might have to wait for a while until the algorithm finishes, and only then can you start driving. The path you will take will be the fastest one (assuming that nothing changed in the external environment).

On the other hand, a greedy algorithm will start you driving immediately and will pick the road that looks the fastest at every intersection. As you can imagine, this strategy might not lead to the fastest arrival time, since you might take some “easy” streets and then find yourself hopelessly stuck in a traffic jam.

Support vector machines

Support vector machines are a type of supervised machine algorithm for learning which is used for classification and regression tasks. Though they are used for both classification and regression, they are mainly used for classification challenges.

Support vector machines are a tool which best serves the purpose of separating two classes. They are a kernel-based algorithm.

• A kernel refers to a function that transforms the input data into a high dimensional space where the question or problem can be solved.
• A kernel function can be either linear or non-linear. Kernel methods are a type of class of algorithms for pattern analysis.
• The primary function of the kernel is to get data as input and transform them into the required forms of output.
• In statistics, “kernel” is the mapping function that calculates and represents values of a 2-dimensional data in a 3-dimensional space format.
• A support vector machine uses a kernel trick which transforms the data to a higher dimension and then it tries to find an optimal hyperplane between the outputs possible.
• Kernel’s method of analysis of data in support vector machine algorithms using a linear classifier to solve non-linear problems is known as ‘kernel trick’.
• Kernels are used in statistics and math, but it is most widely and also most commonly used in support vector machines.

Application of Support Vector Machines

The use of support vector machine algorithms and its examples are used in many technologies which incorporate the use of segregation and distinction.

The real-life applications it range from image classification to face detection, recognition of handwriting and even to bioinformatics.

It allows the classification and categorization of both inductive and transductive models. The support vector machine algorithms make use of training data to segregate different types of documents and flies into different categories.

The segregation done by it is based on the data and score generated by the algorithm and then is compared and contrasted to the initial values provided.