Classification in Very High Dimensional Problems with Handfuls of Examples

Authors:
Mark Palatucci;Tom M. Mitchell
Affiliations:
School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Venue:
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2007

Citing 10
Cited 2

A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling

Machine Learning - Special issue on inductive transfer
Multitask Learning

Machine Learning - Special issue on inductive transfer
Learning to learn: introduction and overview

Learning to learn
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

Data Mining and Knowledge Discovery
Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning to Decode Cognitive States from Brain Images

Machine Learning
Hidden process models

ICML '06 Proceedings of the 23rd international conference on Machine learning
Exploiting parameter domain knowledge for learning in bayesian networks

Exploiting parameter domain knowledge for learning in bayesian networks
Bayesian Network Learning with Parameter Constraints

The Journal of Machine Learning Research
Exploiting temporal information in functional magnetic resonance imaging brain data

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

A supervised clustering approach for fMRI-based inference of brain states

Pattern Recognition
Hybrid random subsample classifier ensemble for high dimensional data sets

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Bayesian framework that shares information between features by modeling similarities between their parameters. We believe this approach is applicable to many sparse, high dimensional problems and especially relevant to those with both spatial and temporal components. One such problem is fMRI time series, and we present a case study that shows how we can successfully classify in this domain with 80,000 original features and only 2 training examples per class.