Exposing Parameters of a Trained Dynamic Model for Interactive Music Creation

As machine learning (ML) systems emerge in end-user applications, learning algorithms and classifiers will need to be robust to an increasingly unpredictable operating environment. In many cases, the parameters governing a learning system cannot be optimized for every user scenario, nor can users typically manipulate parameters defined in the space and terminology of ML. Conventional approaches to user-oriented ML systems have typically hidden this complexity from users by automating parameter adjustment. We propose a new paradigm, in which model and algorithm parameters are exposed directly to end-users with intuitive labels, suitable for applications where parameters cannot be automatically optimized or where there is additional motivation

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Dan Morris, Ian Simon, Sumit Basu
• Keywords: AAAI 2008, Intelligent Agents, ML Systems, Parameters, User-oriented Ml Systems