Teaching Memoryless Randomized Learners Without Feedback

The present paper mainly studies the expected teaching time of memoryless randomized learners without feedback. First, a characterization of optimal randomized learners is provided and, based on it, optimal teaching teaching times for certain classes are established. Second, the problem of determining the optimal teaching time is shown to be NP-hard. Third, an algorithm for approximating the optimal teaching time is given. Finally, two heuristics for teaching are studied, i.e., cyclic teachers and greedy teachers.

• Type: Conference
• Year: 2006
• Where: ALT
• Authors: Frank J. Balbach, Thomas Zeugmann
• Keywords: ALT 2006, Machine Learning, Memoryless Randomized Learners, Optimal Randomized Learners, Optimal Teaching Time