Concepts inImproved classification through runoff elections
Statistical classification
In machine learning and statistics, classification is the problem of identifying which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. The individual observations are analyzed into a set of quantifiable properties, known as various explanatory variables, features, etc. These properties may variously be categorical (e.g.
more from Wikipedia
Two-round system
The two-round system (also known as the second ballot, runoff voting or ballotage) is a voting system used to elect a single winner where the voter casts a single vote for their chosen candidate.
more from Wikipedia
Linear separability
In geometry, two sets of points in a two-dimensional space are linearly separable if they can be completely separated by a single line. In general, two point sets are linearly separable in n-dimensional space if they can be separated by a hyperplane. In more mathematical terms: Let and be two sets of points in an n-dimensional space. Then and are linearly separable if there exists n+1 real numbers, such that every point satisfies and every point satisfies, where is the i:th component of
more from Wikipedia
Binary classification
Binary classification is the task of classifying the members of a given set of objects into two groups on the basis of whether they have some property or not. Some typical binary classification tasks are medical testing to determine if a patient has certain disease or not (the classification property is the disease) quality control in factories; i.e.
more from Wikipedia
Pattern recognition
In machine learning, pattern recognition is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam" or "non-spam"). However, pattern recognition is a more general problem that encompasses other types of output as well.
more from Wikipedia
Support vector machine
A support vector machine (SVM) is a concept in statistics and computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the input, making the SVM a non-probabilistic binary linear classifier.
more from Wikipedia