Discriminative model

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Families and types

Generalized linear regression Binary Categorical Maximum entropy classifiers Boosting (meta-algorithm)

12 related topics

Definition

Joint probability distribution Object recognition Conditional probability distribution Probability distribution Classifiers

11 related topics

Overview

Classification Machine learning Classification Regression Binary classification

17 related topics

Training objectives and Optimizations in applications

Principal component analysis Linear discriminant analysis

2 related topics

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Overview

Classification Statistical classification
Machine learning
Classification
Regression Regression analysis
Binary classification
Prediction error Mean squared prediction error
Loss function
Generative models Generative model
Logistic regression
Conditional random fields Conditional random field
Decision trees
Bayes' theorem
Joint distribution
Classification Classification (machine learning)
Supervised Supervised learning
Unsupervised learning
Posterior possibility Posterior probability

Definition

Typical discriminative modelling approaches

Training objectives and Optimizations in applications

Families and types

Generalized linear regression Generalized linear model
Binary Bernoulli distribution
Categorical Categorical distribution
Maximum entropy classifiers Maximum entropy classifier
Boosting (meta-algorithm)
Linear regression
Computer vision
Random forests Random Forest
K-nearest neighbors algorithm
Support Vector Machines
Decision Tree Learning
Maximum-entropy Markov models Maximum-entropy Markov model

Sources

ISBN ISBN (identifier)
Mitchell, Tom M. Tom M. Mitchell
Ng, Andrew Y. Andrew Ng
Jordan, Michael I. Michael I. Jordan

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Map overview Semantic statistics

Nodes56

Edges55

Triples20

Avg. degree1.96

Density0.035714

Components1

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Discriminative model

Top relations

related to Families and types · 6

Discriminative model → Boosting, Conditional, Examples, Logistic, Markov, Vector MachinesDecision Tree LearningMaximum-entropy

related to Contrast with generative model · 5

Discriminative model → Classifiers, In, It, Terminology, These

related to Definition · 4

Discriminative model → For, Note, Unlike, Within

is a · 2

Discriminative model → model of the conditional probability of the target Y, model of the conditional probability P

Important terminology Word statistics

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Important terminology

discriminative model models generative classification displaystyle used conditional regression probability learning distribution also joint logistic data training given decision classifiers

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Discriminative model	is a	model of the conditional probability P	0.90	text
Discriminative model	is a	model of the conditional probability of the target Y	0.90	text
classification	instance of	for tasks	0.80	text
regression that do not require the joint distribution	instance of	for tasks	0.80	text
discriminative models can yield superior performance	instance of	for tasks	0.80	text
Discriminative model	related to Contrast with generative model	In	0.60	section
Discriminative model	related to Contrast with generative model	These	0.60	section
Discriminative model	related to Contrast with generative model	Terminology	0.60	section
Discriminative model	related to Contrast with generative model	It	0.60	section
Discriminative model	related to Contrast with generative model	Classifiers	0.60	section
Discriminative model	related to Definition	Unlike	0.60	section
Discriminative model	related to Definition	For	0.60	section

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Families and types

Definition

Overview

Training objectives and Optimizations in applications

Key facts & relationships

Topics to explore

Overview

Definition

Typical discriminative modelling approaches

Training objectives and Optimizations in applications

Families and types

Sources

Advanced semantic analysis

Map overview Semantic statistics

Discriminative model

How this topic connects Entity context

Discriminative model

Top relations

Important terminology Word statistics

Important terminology

Entity relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges