Category

Classification algorithms

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artificial neural network

computational model used in machine learning, based on connected, hierarchical functions

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.

support vector machine

set of methods for supervised statistical learning

statistical classification

problem of identifying to 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

statistical algorithm that is used to cluster points of data in functional groups

naive Bayes classifier

classification algorithm

k-nearest neighbors algorithm

classification algorithm

multilayer perceptron

type of feedforward neural network with multiple fully connected layers

decision tree learning

machine learning algorithm

case-based reasoning

approach to solve new case on solution of similar previous case

linear discriminant analysis

method used in statistics, pattern recognition and machine learning

decision trees algorithm

ensemble meta-algorithm for reducing bias and variance in machine learning

nearest neighbor search

(as a form of proximity search (metric space)) optimization problem of finding the point in a given set that is closest (or most similar) to a given point

decision tree algorithm

Linear classifier

statistical classification in machine learning

gradient boosting

machine learning technique

class of algorithms for pattern analysis

AdaBoost (short for Adaptive Boosting) is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many types of learning algorithm to improve performance. The output of multiple weak learners is combined into a weighted sum that represents the final output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals of real values.

statistical regression where the dependent variable can take only two values, to estimate the probability that an observation with particular characteristics will fall into one of the categories

multiclass classification

problem of classifying instances into one of three or more classes

latent class model

model for clustering multivariate discrete data

radial basis function network

an artificial neural network that uses radial basis functions as activation functions

multinomial logistic regression

regression for more than two discrete outcomes

probabilistic latent semantic analysis

Method for analyzing semantic data

generalization error

in machine learning, a measure of how accurately an algorithm is able to predict outcome values for previously unseen data

Decision boundary

Boundary used within classification

relevance vector machine

machine learning technique

Locality-sensitive hashing

method of dimension reduction in which closer items have greater probability of being mapped to the same hash bucket

multi-label classification

variants of the classification problem where multiple labels may be assigned to each instance

Group method of data handling

family of inductive algorithms

Information gain in decision trees

Gain from observing another random variable

Learning Vector Quantization

large margin nearest neighbor

statistical machine learning algorithm for metric learning