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19 January - 15 November 2006
Touch Clarity (www.touchclarity.com) provides real time optimisation of websites. Touch Clarity chooses, from a number of options, the most popular content to display on a page. This decision is made by tracking how many visitors respond to each of the options, by clicking on them. This is a direct commercial application of the multi armed bandit problem - each of the items which might be shown is a separate bandit, with a separate response rate.
As in the multi armed bandit problem, there is a trade off between exploration and exploitation - it is necessary to sometimes serve items other than the most popular in order to measure their response rate with sufficient precision to correctly identify which is the most popular. However, in this application there is a further complication - typically the rates of response to each item will vary over time, so continuous exploration is necessary in order to track this time variation, as old knowledge becomes out of date. An extreme example of this might be in choosing which news story to serve as the main story on a news page - interest in one story will decrease over time while interest in another will increase. In addition, the interest in several stories might vary in a similar, coherent way - for example a general increase in interest in sports stories at weekends, or in political stories near to an election. So there are typically two types of variation to consider - where response rates vary together, and where response rates vary completely independently.
1 January - 30 June 2006
The Visual Object Classes Chellenges has the following objectives:
• To compile a standardised collection of object recognition databases
• To provide standardised ground truth object annotations across all databases
• To provide a common set of tools for accessing and managing the database annotations
• To run a challenge evaluating performance on object class recognition
1 September 2005 - 12 April 2006
The objective of the Challenge is to design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Ideally, these are basic vocabulary units suitable for different tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling.
The scientific goals are:
• To learn of the phenomena underlying word construction in natural languages
• To discover approaches suitable for a wide range of languages
• To advance machine learning methodology
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1 October 2005 - 10 April 2006
Textual Entailment Recognition has been proposed recently as a generic task that captures major semantic inference needs across many natural language processing applications, such as Question Answering (QA), Information Retrieval (IR), Information Extraction (IE), and (multi) document summarisation. This task requires to recognise, given two text fragments, whether the meaning of one text is entailed (can be inferred) from the other text.
By introducing a second challenge we hope to keep the momentum going, and to further promote the formation of a research community around the applied entailment task. As in the previous challenge, the main task is judging whether a hypothesis (H) is entailed by a text (T). One of the main goals for the RTE-2 dataset is to provide more "realistic" text-hypothesis examples, based mostly on outputs of actual systems. We focus on the four application settings mentioned above: QA, IR, IE and multi-document summarisation. Each portion of the dataset includes typical T-H examples that correspond to success and failure cases of such applications. The examples represent different levels of entailment reasoning, such as lexical, syntactic, morphological and logical.
30 July 2005 - 1 April 2006
The objective of the challenge is to develop machine learning methods for structured data mining and to evaluate these methods for XML document mining tasks. The challenge is focused on classification and clustering for XML documents. Datasets coming from different XML collections and covering a variety of classification and clustering situations will be provided to the participants.
One goal of this track is to build a reference categorisation/ clustering corpora of XML documents. The organisers are open to any suggestion concerning the construction of such corpora.
1 October 2005 - 1 March 2006
This project is dedicated to stimulate research and reveal the state-of-the art in "model selection" by organising a competition followed by a workshop. Model selection is a problem in statistics, machine learning, and data mining. Given training data consisting of input-output pairs, a model is built to predict the output from the input, usually by fitting adjustable parameters. Many predictive models have been proposed to perform such tasks, including linear models, neural networks, trees, and kernel methods. Finding methods to optimally select models, which will perform best on new test data, is the object of this project. The competition will help identify accurate methods of model assessment, which may include variants of the well-known cross-validation methods and novel techniques based on learning theoretic performance bounds. Such methods are of great practical importance in pilot studies, for which it is essential to know precisely how well desired specifications are met.
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