Prototype applications of instance-based reasoning.

The thesis focuses on instance-based learning methods. It is concerned with two particular prototype practical applications. The first presented domain regards a task of gas prediction consumption, the second one deals with early detection of pump faults. These domains represent diverse learning tasks with different singularities, targets, feature definition and characteristics. The process of their solution proved the instance-based learning represents an advisable learning paradigm for their solution. It suggested that practical applications of learning methods can be rarely restricted to a single learning algorithm without problem-oriented modifications. The final solution of the gas prediction task is represented by the meta-learner combining three base prediction methods. The suggested solution of the pump fault detection is purely instance-based - CED approach - but it has to regard the specific existence of the fault scales.Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi