Hasty generalization — is a logical fallacy of faulty generalization by reaching an inductive generalization based on insufficient evidence essentially making a hasty conclusion without considering all of the variables. In statistics, it may involve basing broad… … Wikipedia
Universal law of generalization — The universal law of generalization is a theory of cognition originally posited by Roger Shepard. According to it, the probability that a response to one stimulus will be generalized to another will be a function of the distance between the two… … Wikipedia
Concatenated error correction code — In coding theory, concatenated codes form a class of error correcting codes that are derived by combining an inner code and an outer code. They were conceived in 1966 by Dave Forney as a solution to the problem of finding a code that has both… … Wikipedia
Margin classifier — In machine learning, a margin classifer is a classifier which is able to give an associated distance from the decision boundary for each example. For instance, if a linear classifier (e.g. perceptron or linear discriminant analysis) is used, the… … Wikipedia
Artificial neural network — An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an… … Wikipedia
BrownBoost — is a boosting algorithm that may be robust to noisy datasets. BrownBoost is an adaptive version of the boost by majority algorithm. As is true for all boosting algorithms, BrownBoost is used in conjunction with other machine learning methods.… … Wikipedia
Random forest — In machine learning, a random forest is a classifier that consists of many decision trees and outputs the class that is the mode of the classes output by individual trees. The algorithm for inducing a random forest was developed by Leo Breiman… … Wikipedia
Probably approximately correct learning — In computational learning theory, probably approximately correct learning (PAC learning) is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant.[1] In this framework, the learner receives samples… … Wikipedia
Categorical imperative — Part of a series on Immanue … Wikipedia
Support vector machine — Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Viewing input data as two sets of vectors in an n dimensional space, an SVM will construct a separating hyperplane in that… … Wikipedia
