Machine Learning - Special issue on inductive transfer
A theoretical framework for learning from a pool of disparate data sources
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Task clustering and gating for bayesian multitask learning
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Application and development of new learning methodologies for fmri data analysis
Application and development of new learning methodologies for fmri data analysis
SVM+ regression and multi-task learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning using hidden information (learning with teacher)
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Enhanced default risk models with SVM+
Expert Systems with Applications: An International Journal
Privileged information for data clustering
Information Sciences: an International Journal
Generalized locality preserving Maxi-Min Margin Machine
Neural Networks
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Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of diagnostic models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating a classifier. This approach, known as standard inductive learning, is used in most application studies. Recently, several new learning methodologies have emerged. For instance, when training data can be naturally separated into several groups (or structured), we can view model estimation for each group as a separate task, leading to a Multi-Task Learning framework. Similarly, a setting where the training data are structured, but the objective is to estimate a single predictive model (for all groups), leads to the Learning with Structured Data and SVM+ methodology recently proposed by Vapnik [(2006). Empirical inference science afterword of 2006. Springer]. This paper describes a biomedical application of these new data modeling approaches for modeling heterogeneous data using several medical data sets. The characteristics of group variables are analyzed. Our comparisons demonstrate the advantages and limitations of these new approaches, relative to standard inductive SVM classifiers.