Data World Blog

Support Vector Machines (SVM) learning combines of both the instance-based nearest neighbor algorithm and the linear regression modeling. Support Vector Machines can be imagined as a surface that creates a boundary (hyperplane) between points of data plotted in multidimensional that represents examples and their feature values. Since it is likely that the line that leads to the greatest separation will generalize the best to the future data, SVM involves a search for the Maximum Margin Hyperplane (MMH) that creates the greatest separation between the 2 classes. If the data ara not linearly separable is used a slack variable, which creates a soft margin that allows some points to fall on the incorrect side of the margin. But, in many real-world applications, the relationship between variables are nonlinear. A key featureof the SVMs are their ability to map the problem to a higher dimension space using a process known as the Kernel trick, this involves a process of constructing ne...

An Artificial Neural Network (ANN) models the relationship between a set of input signals and an output signal using a model derived from our undestanding of how a biological brain responds to stimuli from sensory inputs. ANN uses a network of artificial neurons or nodes to solve learning problems. At first ANNs were used to simulate learning simple functions like the  AND  function or the logical  OR  function, but nowadays as cumputers have become more powerfull that complexity of the ANNs has increased so much that they are now frequently applied to more practical problems including speech and handwrinting recognition programs, automation of smart devices, sophisticated models of weather and climate patterns, etc… ANNs are very versatile learners that can be applied to nearly any learning task, classification, numeric prediction, and even unsuppervised pattern recognition. ANNs are best applied to problems where the input data and output data are well defin...