Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification and regression in Machine Learning problems.
The idea behind SVMs is to find a hyperplane that optimally separates the different classes of data. In the case of binary classification, the hyperplane divides the space into two regions, one for each class. In the case of regression, a hyperplane is sought that best fits the data.
To find the optimal hyperplane, SVMs seek to maximize the distance between the closest points of each class (called support vectors), known as the maximum margin. In case the data are not linearly separable, kernel techniques are used to transform the feature space into one of higher dimensionality where they can be separable.
SVMs are widely used in data classification in areas such as biology, finance and marketing, as well as in fraud detection, image recognition and natural language processing.
Artificial intelligence (AI) can change the way sales channels and customers are managed for manufacturers and distributors of consumer products, and can [...]
Read More »More and more companies are taking advantage of the relevant information they extract from the data they possess and generate to improve their processes and discover new ways to [...]
Read More »In the dynamic financial world, optimizing the return on available assets is essential to the success of any lender. Gam [...]
Read More »Normally the acronym NPLs (Non Performing Loans) is used in the financial sector and is a reality in Spanish banks as well as in banks [...].
Read More »