Translations:Transfer Learning/6/en: Difference between revisions

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Message definition (Transfer Learning)

Formally, a '''domain''' <math>\mathcal{D} = \{\mathcal{X}, P(X)\}</math> consists of a {{Term|latent space|feature space}} <math>\mathcal{X}</math> and a marginal distribution <math>P(X)</math>. A '''task''' <math>\mathcal{T} = \{\mathcal{Y}, f(\cdot)\}</math> consists of a label space <math>\mathcal{Y}</math> and a predictive function <math>f</math>. Transfer learning applies when the source and target differ in domain, task, or both.

Formally, a domain $\mathcal{D} = \{\mathcal{X}, P(X)\}$ consists of a feature space $\mathcal{X}$ and a marginal distribution $$ P(X) $$ . A task $\mathcal{T} = \{\mathcal{Y}, f(\cdot)\}$ consists of a label space $\mathcal{Y}$ and a predictive function $$ f $$ . Transfer learning applies when the source and target differ in domain, task, or both.

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	Formally, a '''domain''' <math>\mathcal{D} = \{\mathcal{X}, P(X)\}</math> consists of a feature space <math>\mathcal{X}</math> and a marginal distribution <math>P(X)</math>. A '''task''' <math>\mathcal{T} = \{\mathcal{Y}, f(\cdot)\}</math> consists of a label space <math>\mathcal{Y}</math> and a predictive function <math>f</math>. Transfer learning applies when the source and target differ in domain, task, or both.		Formally, a '''domain''' <math>\mathcal{D} = \{\mathcal{X}, P(X)\}</math> consists of a {{Term\|latent space\|feature space}} <math>\mathcal{X}</math> and a marginal distribution <math>P(X)</math>. A '''task''' <math>\mathcal{T} = \{\mathcal{Y}, f(\cdot)\}</math> consists of a label space <math>\mathcal{Y}</math> and a predictive function <math>f</math>. Transfer learning applies when the source and target differ in domain, task, or both.