Support Vector Machine is one of the most commonly used supervised machine learning algorithms for data classification. A binary classifier, the support vector machine algorithm works in vector space to sort data points by finding the best hyperplane separating them into two groups. Thanks to its reliance upon vectors, it finds frontiers between groups of data points even in nonlinear patterns and features spaces of high dimensions.
K Nearest Neighbors is a popular classification algorithm for supervised machine learning. It permits to divide data points into groups, defining a model that will then be able to classify an unknown data point in one group or another. The K parameter, defined during programming, allows the algorithm to classify unknown data points by examining the K closest known data points.
In a semantic net, a diagram of relations between objects, essential notions can be defined as follows:
Combinators: linking objects together
Reification: actions implying results
Localization: a frame where objects and actions happen
Story sequence: a series of actions happening linearly in time
In natural language, knowledge is generally organized from general categories to basic objects and finally specific objects.
Another element of language is recording change in the evolution of objects during the unfolding of stories.
Language also tracks movement in the description of actions.
An agent makes an object move from a source to a destination using a instrument. A co-agent may be involved, the action might be aimed towards a beneficiary, and the motion may be helped by a conveyance, etc. In English, preposition tend to be used to define the role of each part in the action, enabled recording of interactions.
Agents’ action determine transitions in the semantic net, which result in trajectories.
Each type of story implies a number of characteristics that correspond to the situation. Example: events can be disasters or parties, they have a time and place, involved people, casualties, money, places…