2013/03/27 by Larry Rendell, Rendell, Larry
Mathematics · Computer Science · #Statistics Education and Methodologies #Machine Learning and Algorithms #AI-based Problem Solving and Planning
paper · pdf · doi:10.48550/arxiv.1304.3433
This paper examines some methods and ideas underlying the author's successful probabilistic learning systems(PLS), which have proven uniquely effective and efficient in generalization learning or induction. While the emerging principles are generally applicable, this paper illustrates them in heuristic search, which demands noise management and incremental learning. In our approach, both task performance and learning are guided by probability. Probabilities are incrementally normalized and revised, and their errors are located and corrected.