By O'sullivan F., Roy S.

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Additional info for A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data (2003)(en)(16s

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1. RADIAL BASIS FUNCTION NEURAL NETWORKS We chose Arti¢cial Neural Networks because of their ability to recognize patterns in the presence of noise and from sparse and/or incomplete data. They perform matching in high-dimensional spaces, e¡ectively interpolating and extrapolating from learned data. We chose Radial Basis Function (RBF) networks (Moody & Darken 1989) because they are universal approximators and train rapidly; usually orders of magnitude faster than back propagation. Their rapid training makes them suitable for applications where on-line, incremental learning is desired such as a set-top box observing TV viewing.

TiVos generate personalized recommendations that are displayed to users. Their recommender learns by tracking which programs users choose to record and user feedback of ‘thumbs-up’ or ‘thumbs-down’ to indicate how they feel about TV shows (on a 1 to 7 scale). 30 JOHN ZIMMERMAN ET AL. Predictive Media, Inc. provides another commercially available recommender system. They use a mixed model approach that combines statistical analysis, expert systems, and neural networks to generate content recommendations.

However, we did not evaluate the Explicit Preferences Expert that simply propagates the declared user preferences in the General Ontology. We started from the Stereotypical UM Expert. We simulated an initial scenario where the user has speci¢ed her personal data and general interests, but where she does not declare her TV program preferences. Thus, the recommendations are based only on the stereotypical information. In this ¢rst phase, we evaluated the correctness of the stereotypical classi¢cation and the accuracy of recommendations by feeding the system with the socio-demographic data and the general interests (dataset a) collected by means of the interviews.

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