Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Machine Learning - Special issue on inductive transfer
Learning to learn
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Schema Analysis and Reconciliation Tool Environment for Heterogeneous Databases
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Semi-Automatic, Semantic Discovery of Properties from Database Schemes
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
A model of inductive bias learning
Journal of Artificial Intelligence Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A distributed learning framework for heterogeneous data sources
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multiple information sources cooperative learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SVM+ regression and multi-task learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Predictive learning with sparse heterogeneous data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Collaborative boosting for activity classification in microblogs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task. For example, aiming towards the design of an automated diagnostic tool for some disease, one may wish to integrate data gathered in many different hospitals. A major obstacle to such endeavors is that different data sources may vary considerably in the way they choose to represent related data. In practice, the problem is usually solved by a manual construction of semantic mappings and translations between the different sources. Recently there have been attempts to introduce automated algorithms based on machine learning tools for the construction of such translations.In this work we propose a theoretical framework for making classification predictions from a collection of different data sources, without creating explicit translations between them. Our framework allows a precise mathematical analysis of the complexity of such tasks, and it provides a tool for the development and comparison of different learning algorithms. Our main objective, at this stage, is to demonstrate the usefulness of computational learning theory to this practically important area and to stimulate further theoretical and experimental research of questions related to this framework.