Prediction and Change Detection in Sequential Data for Interactive Applications

We consider the problems of sequential prediction and change detection that arise often in interactive applications: A semi-automatic predictor is applied to a time-series and is expected to make proper predictions and request new human input when change points are detected. Motivated by the Transductive Support Vector Machines (Vapnik 1998), we propose an online framework that naturally addresses these problems in a unified manner. Our empirical study with a synthetic dataset and a road tracking dataset demonstrate the efficacy of the proposed approach.

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Jun Zhou, Li Cheng, Walter F. Bischof
• Keywords: AAAI 2008, Intelligent Agents, Road Tracking Dataset, Semi-automatic Predictor, Transductive Support Vector