There are a number of machine learning algorithms in the world that have a vast number of uses. Few of these algorithms have the same utility, however, as the support vector machine. A support vector machine may not sound as simple or as straightforward as a decision tree or a linear regression algorithm. But that does not mean it is not as effective. The support vector machine is able to map, sort, and predict the trend of a data set easily displayed men. These multiple uses make it easy to implement and run the support vector machine until it is able to accurately analyze and display information for your data mining needs.
What is the Support Vector Machine Algorithm?
The support vector machine is an algorithm that is primarily focused on detecting and analyzing relationships. This machine learning algorithm works by analyzing data sets through a series of variables. The way that the data respond to the variables can be mapped out. This machine draws a two-dimensional space and relates each data point to one of the different categories. Data points are then mapped onto space with a line or curve down the middle to separate the different categories. Then, each new piece of data or extra part of the data set is mapped onto the plot and the category is derived from its placement. In the case of regression, the algorithm uses the points on the plot to draw a regression line that maps the data.
Support Vector Machine Algorithm Types
An algorithm like the support vector has to have an architecture to work off of. The artificial intelligence architecture must allow for the algorithm to process data while also providing flexibility for the machine to learn. A common machine learning architecture is the artificial neural network. Artificial neural networks are based on the human brain and consist of a large number of connected nodes. The nodes contain different aspects of the algorithm and are weighted. Machines learn by processing data through the nodes, checking the output, and then shifting the weighting of the nodes depending on the variance from the example set or the unsupervised guidelines. In a support vector machine algorithm, nodes may be parts of the original function or different variables and categories used.
There are a number of different approaches to learning that the support vector machine algorithm can perform. One of these is supervised learning. Supervised learning occurs when a machine learning algorithm attempts to replicate an example set within a margin of error. The algorithm repeats over and over until the example set is reached. In the case of a support vector machine algorithm, the example set would be a number of categories with specific data sets in them. There may be a margin of error for a handful of different categories for data points. If the machine is being used for regression, the example set will be a particular regression line through the data. The algorithm will try again and again to replicate that regression line.
There is also the possibility of using the support vector machine algorithm for unsupervised learning. Technically, the support vector machine becomes a support vector algorithm when it is used for unsupervised learning. This form of learning does not work off of an example set. Instead, it uses a series of guidelines to either sort data into categories or draw a regression line. The unsupervised learning system is developed differently than a supervised learning system. That different architecture does not change the basic work or the output for either linear regression or classifying information.
Support Vector Machine Algorithm Uses
As mentioned previously, the two main uses of this algorithm are classification and regression. Classification sorts data into which makes sense of the data and tells the user a considerable amount about it. Categories can help show similarities and differences between what may look like otherwise disparate data points. The process of classification can save time and improve the function of other algorithms that might apply to the data. Regression is another tool similar to classification. In the process of regression, a line is drawn which both indicates the trend of the data and helps to predict future data points. Prediction can also be a result of classification. Putting information into categories can point towards possible future trends.
Another important news is visualization. Many algorithms do not work from a visual model or can be muddled like a random forest. There are few algorithms that present a clean line with complex data. The support vector machine is one of these. A support vector machine can detail a number of different categories and data points and make sense of those data points with a simple square. This approach can be used to visualize hundreds or even thousands of data points on one square. The visualization potential is enhanced further by the ability to draw a regression line. Regression lines can distill the variance between thousands of data points into one line.
Thoughts on Support Vector Machine Algorithm
The support vector machine must be properly used and implemented. It is most applicable in binomial situations where there are only two variables to be plotted on a graph. The operator must also take care to identify whether or not the data is probabilistic. The support vector machine has to handle probabilistic, supervised, and non-binomial data differently. If those simple steps are taken, the support vector machine can be utilized and it’s simple, clean, and efficient way. All data mining firms should consider adding this effective tool to there repertoires.