Machine Learning Summer School 2006 - Canberra

Anti-Learning

author: Adam Kowalczyk, National ICT Australia

Description

The Biological domain poses new challenges for statistical learning. In the talk we shall analyze and theoretically explain some counter-intuitive experimental and theoretical findings that systematic reversal of classifier decisions can occur when switching from training to independent test data (the phenomenon of anti-learning). We demonstrate this on both natural and synthetic data and show that it is distinct from overfitting. The natural datasets discussed will include: prediction of response to chemo-radio-therapy for esophageal cancer from gene expression (measured by cDNA-microarrays); prediction of genes affecting the aryl hydrocarbon receptor pathway in yeast. The main synthetic classification problem will be the approximation of samples drawn from high dimensional distributions, for which a theoretical explanation will be outlined.

Categories

Top: Computer Science: Machine Learning

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*Slides*
0:01	Anti-Learning
3:20	Overview
4:10	Definition of anti-learning
6:11	Anti-learning in Low Dimensions
11:27	Evaluation Measure
14:25	Learning and anti-learning mode of supervised classification
15:47	Anti-learning in Cancer Genomics
15:57	From Oesophageal Cancer to machine learning challenge
20:02	Learning and anti-learning mode of supervised classification
24:58	Anti-learning in Classification of Genes in Yeast
25:29	KDD’02 task: identification of Aryl Hydrocarbon Receptor genes (AHR data)
30:36	KDD Cup 2002 Yeast Gene Regulation Prediction Task http://www.biostat.wisc.edu/~craven/kddcup/task2.ppt
35:30	Anti-learning in High Dimensional Approximation (Mimicry)
36:18	Paradox of High Dimensional Mimicry
41:29	Hadamard Matrix
43:00	Anti-learning in classification of Hadamard dataset

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