A computer-implemented method includes creating a classifier by: training a machine learning model using two or more tasks, wherein the tasks lie in two or more domains; including in the machine learning model at least one attribute common to at least two of said two or more domains; including in the machine learning model at least one latent feature that affects at least two of the two or more tasks that fall within one of the at least two domains; and constructing the classifier based on said machine learning model. The computer-implemented method further includes applying the classifier to at least one operational task.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: creating a classifier by: training a machine learning model using two or more tasks, wherein said two or more tasks lie in two or more domains; including in said machine learning model at least one attribute common to at least two of said two or more domains; including in said machine learning model at least one latent feature that affects at least two of said two or more tasks that fall within one of said at least two domains; and constructing said classifier based on said machine learning model; and applying said classifier to an operational task. 2. The computer-implemented method of claim 1, wherein said operational task is selected from the group consisting of: (a) a new task in a previously trained domain; (b) a previously trained task in a new domain; and (c) a new task in a new domain. 3. The computer-implemented method of claim 1, wherein: including in said machine learning model at least one attribute common to at least two of said two or more domains comprises applying supervised machine learning; and including in said machine learning model at least one latent feature that affects at least two of said two or more tasks that fall within one of said at least two domains comprises applying unsupervised machine learning. 4. The computer-implemented method of claim 3, wherein said supervised machine learning comprises labeled latent Dirichlet allocation. 5. The computer-implemented method of claim 3, wherein said unsupervised machine learning comprises discriminative latent Dirichlet allocation. 6. The computer-implemented method of claim 1, wherein said classifier is operable to apply derived information both concurrently and sequentially to said operational task. 7. The computer-implemented method of claim 1, wherein: said classifier provides a latent generative model that includes sets of observations of one or more topics explained by one or more unobserved groups in at least one of: one or more of said two or more tasks, and one or more of said two or more domains; and said latent generative model is applied to said operational task. 8. A computer program product comprising one or more computer readable storage media and program instructions stored on said one or more computer readable storage media, said program instructions comprising instructions to: create a classifier by: training a machine learning model using two or more tasks, wherein said two or more tasks lie in two or more domains; including in said machine learning model at least one attribute common to at least two of said two or more domains; including in said machine learning model at least one latent feature that affects at least two of said two or more tasks that fall within one of said at least two domains; and constructing said classifier based on said machine learning model; and apply said classifier to an operational task. 9. The computer program product of claim 8, wherein said operational task is selected from the group consisting of: (a) a new task in a previously trained domain; (b) a previously trained task in a new domain; and (c) a new task in a new domain. 10. The computer program product of claim 8, wherein: said instructions to create a classifier by including in said machine learning model at least one attribute common to at least two of said two or more domains comprises applying supervised machine learning; and including in said machine learning model at least one latent feature that affects at least two of said two or more tasks that fall within one of said at least two domains comprises applying unsupervised machine learning. 11. The computer program product of claim 10, wherein said supervised machine learning comprises labeled latent Dirichlet allocation. 12. The computer program product of claim 10, wherein said unsupervised machine learning comprises discriminative latent Dirichlet allocation. 13. The computer program product of claim 8, wherein said classifier is operable to apply derived information both concurrently and sequentially to said operational task. 14. The computer program product of claim 8, wherein: said classifier provides a latent generative model that includes sets of observations of one or more topics explained by one or more unobserved groups in at least one of: one or more of said two or more tasks, and one or more of said two or more domains; and said latent generative model is applied to said operational task. 15. A computer system comprising: one or more processors; one or more computer readable storage media; computer program instructions; said computer program instructions being stored on said one or more computer readable storage media; said computer program instructions comprising instructions to: create a classifier by: training a machine learning model using two or more tasks, wherein said two or more tasks lie in two or more domains; including in said machine learning model at least one attribute common to at least two of said two or more domains; including in said machine learning model at least one latent feature that affects at least two of said two or more tasks that fall within one of said at least two domains; and constructing said classifier based on said machine learning model; and apply said classifier to an operational task. 16. The computer system of claim 15, wherein said operational task is selected from the group consisting of: (a) a new task in a previously trained domain; (b) a previously trained task in a new domain; and (c) a new task in a new domain. 17. The computer system of claim 15, wherein: said computer program instructions to create a classifier by including in said machine learning model at least one attribute common to at least two of said two or more domains comprises applying supervised machine learning; and including in said machine learning model at least one latent feature that affects at least two of said two or more tasks that fall within one of said at least two domains comprises applying unsupervised machine learning. 18. The computer system of claim 17, wherein said supervised machine learning comprises labeled latent Dirichlet allocation. 19. The computer system of claim 17, wherein said unsupervised machine learning comprises discriminative latent Dirichlet allocation. 20. The computer system of claim 15, wherein said classifier is operable to apply derived information both concurrently and sequentially to said operational task.