Unsupervised and Transfer Learning Challenge 2011

Unsupervised and Transfer Learning Challenge 2011 targeted classification problems, which are found in many application domains, including in pattern recognition (classification of images or videos, speech recognition), medical diagnosis, marketing (customer categorization), and text categorization (filtering of spam), using unsupervised and transfer learning algorithms. Predictive models capable of classifying new instances usually require “training” (parameter adjustment) using large amounts of labeled training data (pairs of examples of instances and associated labels). Unfortunately, few labeled training data may be available due to the cost or burden of manually annotating data. Recent research has been focusing on making use of the vast amounts of unlabeled data available at low cost including: space transformations, dimensionality reduction, hierarchical feature representations, and kernel learning. However, these advances tend to be ignored by practitioners who continue using a handful of popular algorithms like PCA, ICA, k-means, and hierarchical clustering. This challenge evaluated unsupervised and transfer learning algorithms free of inventor bias. One of our databases, Avicenna database, has been used in this challenge.

Avicenna database is available on the website of this challenge:

http://www.causality.inf.ethz.ch/ul_data/AVICENNA.html

The results of the challenge are avaiable here. The results are also reported in Workshop on Unsupervised and Transfer Learning (ICML'11) and Autonomous and Incremental Learning (IJCNN'11).

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