Discriminative model, the Glossary

Table of Contents

1. 31 relations: Bayes' theorem, Bernoulli distribution, Binary classification, Boosting (machine learning), Categorical distribution, Conditional probability distribution, Conditional random field, Cross-entropy, Decision tree, Generalized linear model, Generative adversarial network, Generative model, Joint probability distribution, Linear classifier, Linear discriminant analysis, Linear regression, Logistic regression, Loss function, Microsoft, Mixture model, Multinomial logistic regression, Naive Bayes classifier, Outline of object recognition, Posterior probability, Principal component analysis, Probability distribution, Random forest, Regression analysis, Statistical classification, Supervised learning, Unsupervised learning.

2. Regression models

Bayes' theorem

Bayes' theorem (alternatively Bayes' law or Bayes' rule, after Thomas Bayes) gives a mathematical rule for inverting conditional probabilities, allowing us to find the probability of a cause given its effect.

See Discriminative model and Bayes' theorem

Bernoulli distribution

In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, is the discrete probability distribution of a random variable which takes the value 1 with probability p and the value 0 with probability q.

See Discriminative model and Bernoulli distribution

Binary classification

Binary classification is the task of classifying the elements of a set into one of two groups (each called class).

See Discriminative model and Binary classification

Boosting (machine learning)

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance.

See Discriminative model and Boosting (machine learning)

Categorical distribution

In probability theory and statistics, a categorical distribution (also called a generalized Bernoulli distribution, multinoulli distribution) is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each category separately specified.

See Discriminative model and Categorical distribution

Conditional probability distribution

In probability theory and statistics, the conditional probability distribution is a probability distribution that describes the probability of an outcome given the occurrence of a particular event.

See Discriminative model and Conditional probability distribution

Conditional random field

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction.

See Discriminative model and Conditional random field

Cross-entropy

In information theory, the cross-entropy between two probability distributions p and q, over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution q, rather than the true distribution p.

See Discriminative model and Cross-entropy

Decision tree

A decision tree is a decision support hierarchical model that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

See Discriminative model and Decision tree

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. Discriminative model and generalized linear model are regression models.

See Discriminative model and Generalized linear model

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI.

See Discriminative model and Generative adversarial network

Generative model

In statistical classification, two main approaches are called the generative approach and the discriminative approach.

See Discriminative model and Generative model

Joint probability distribution

Given two random variables that are defined on the same probability space, the joint probability distribution is the corresponding probability distribution on all possible pairs of outputs.

See Discriminative model and Joint probability distribution

Linear classifier

In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to.

See Discriminative model and Linear classifier

Linear discriminant analysis

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events.

See Discriminative model and Linear discriminant analysis

Linear regression

In statistics, linear regression is a statistical model which estimates the linear relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables).

See Discriminative model and Linear regression

Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables. Discriminative model and logistic regression are regression models.

See Discriminative model and Logistic regression

Loss function

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.

See Discriminative model and Loss function

Microsoft

Microsoft Corporation is an American multinational corporation and technology company headquartered in Redmond, Washington.

See Discriminative model and Microsoft

Mixture model

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.

See Discriminative model and Mixture model

Multinomial logistic regression

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. Discriminative model and multinomial logistic regression are regression models.

See Discriminative model and Multinomial logistic regression

Naive Bayes classifier

In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class.

See Discriminative model and Naive Bayes classifier

Outline of object recognition

Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence.

See Discriminative model and Outline of object recognition

Posterior probability

The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule.

See Discriminative model and Posterior probability

Principal component analysis

Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.

See Discriminative model and Principal component analysis

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of possible outcomes for an experiment.

See Discriminative model and Probability distribution

Random forest

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time.

See Discriminative model and Random forest

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features').

See Discriminative model and Regression analysis

Statistical classification

When classification is performed by a computer, statistical methods are normally used to develop the algorithm.

See Discriminative model and Statistical classification

Supervised learning

Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model.

See Discriminative model and Supervised learning

Unsupervised learning

Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data.

See Discriminative model and Unsupervised learning

See also

Regression models

• Additive model
• Bradley–Terry model
• Censored regression model
• Choice modelling
• Conditional change model
• Discriminative model
• EM algorithm and GMM model
• Errors-in-variables models
• Factor regression model
• Fay–Herriot model
• First-hitting-time model
• Fixed effects model
• GHK algorithm
• General linear model
• Generalized additive model
• Generalized linear array model
• Generalized linear model
• Generalized linear models
• Hedonic regression
• Hierarchical generalized linear model
• History index model
• Linear model
• Logistic regression
• Marginal model
• Mixed model
• Multi-attribute global inference of quality
• Multilevel model
• Multilevel modeling for repeated measures
• Multilevel regression with poststratification
• Multinomial logistic regression
• Multivariate probit model
• Nonlinear mixed-effects model
• Polynomial and rational function modeling
• Proper linear model
• Random effects model
• Regression dilution
• Response modeling methodology
• Segmented regression
• Simultaneous equations model
• Sinusoidal model
• Stock sampling
• Structural equation modeling
• Threshold model
• Time series models
• Tobit model
• Total least squares
• Truncated normal hurdle model
• Truncated regression model
• Vector generalized linear model

References

[1] https://en.wikipedia.org/wiki/Discriminative_model

Also known as Conditional model.