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
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Predictive learning with sparse heterogeneous data
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
Tree ensembles for predicting structured outputs
Pattern Recognition
| Hi-index | 0.00 |
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik [9] proposed general approach to formalizing such problems, known as Learning With Structured Data (LWSD) and its SVM-based optimization formulation called SVM+. Liang and Cherkassky [5,6] showed empirical validation of SVM+ for classification, and its connections to Multi-Task Learning (MTL) approaches in machine learning. This paper builds upon this recent work [5,6,9] and describes a new methodology for regression problems, combining Vapnik's SVM+ regression [9] and the MTL classification setting [6], for regression problems. We also show empirical comparisons between standard SVM regression, SVM+, and proposed SVM+MTL regression method. Practical implementation of new learning technologies, such as SVM+, is often hindered by their complexity, i.e, large number of tuning parameters (vs standard inductive SVM regression). To this end, we provide a practical scheme for model selection that combines analytic selection of parameters for SVM regression [3] and resampling-based methods for selecting model parameters specific to SVM+ and SVM+MTL.