Inductive transfer

Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited. Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited. In 1993, Lorien Pratt published a paper on transfer in machine learning, formulating the discriminability-based transfer (DBT) algorithm. In 1997, the journal Machine Learning published a special issue devoted to transfer learning, and by 1998, the field had advanced to include multi-task learning, along with a more formal analysis of its theoretical foundations. Learning to Learn, edited by Pratt and Sebastian Thrun, is a 1998 review of the subject. Transfer learning has also been applied in cognitive science, with the journal Connection Sciencepublishing a special issue on reuse of neural networks through transfer in 1996. The definition of transfer learning is given in terms of domain and task. The domain D {displaystyle {mathcal {D}}} consists of: a feature space X {displaystyle {mathcal {X}}} and a marginal probability distribution P ( X ) {displaystyle P(X)} , where X = { x 1 , . . . , x n } ∈ X {displaystyle X={x_{1},...,x_{n}}in {mathcal {X}}} . Given a specific domain, D = { X , P ( X ) } {displaystyle {mathcal {D}}={{mathcal {X}},P(X)}} , a task consists of two components: a label space Y {displaystyle {mathcal {Y}}} and an objective predictive function f ( ⋅ ) {displaystyle f(cdot )} (denoted by T = { Y , f ( ⋅ ) } {displaystyle {mathcal {T}}={{mathcal {Y}},f(cdot )}} ), which is learned from the training data consisting of pairs , which consist of pairs { x i , y i } {displaystyle {x_{i},y_{i}}} , where x i ∈ X {displaystyle x_{i}in X} and y i ∈ Y {displaystyle y_{i}in {mathcal {Y}}} . The function f ( ⋅ ) {displaystyle f(cdot )} can be used to predict the corresponding label, f ( x ) {displaystyle f(x)} , of a new instance x {displaystyle x} . Given a source domain D S {displaystyle {mathcal {D}}_{S}} and learning task T S {displaystyle {mathcal {T}}_{S}} , a target domain D T {displaystyle {mathcal {D}}_{T}} and learning task T T {displaystyle {mathcal {T}}_{T}} , transfer learning aims to help improve the learning of the target predictive function f T ( ⋅ ) {displaystyle f_{T}(cdot )} in D T {displaystyle {mathcal {D}}_{T}} using the knowledge in D S {displaystyle {mathcal {D}}_{S}} and T S {displaystyle {mathcal {T}}_{S}} , where D S ≠ D T {displaystyle {mathcal {D}}_{S} eq {mathcal {D}}_{T}} , or T S ≠ T T {displaystyle {mathcal {T}}_{S} eq {mathcal {T}}_{T}} . Algorithms are available for transfer learning in Markov logic networks and Bayesian networks. Transfer learning has also been applied to cancer subtypediscovery, building utilization, general game playing, text classification and spam filtering.