Machine Learning: Proceedings of the Ninth International Workshop (ML92) covers the papers and posters presented at ML92, the Ninth International Machine Learning Conference, held at Aberdeen, Scotland on July 1-3, 1992. The book focuses on the advancements of practices, methodologies, approaches, and techniques in machine learning. The selection first offers information on the principal axes method for constructive induction; learning by incomplete explanations of failures in recursive domains; and eliminating redundancy in explanation-based learning. Topics include means-ends analysis search in recursive domains, description space transformation, distance metric, generating similarity matrix, and learning principal axes. The text then examines trading off consistency and efficiency in version-space induction; improving path planning with learning; finding the conservation of momentum; and learning to predict in uncertain continuous tasks. The manuscript elaborates on a teaching method for reinforcement learning, compiling prior knowledge into an explicit bias, spatial analogy and subsumption, and multistrategy learning with introspective meta-explanations. The publication also ponders on selecting typical instances in instance-based learning and temporal difference learning of backgammon strategy. The selection is a valuable source of information for researchers interested in machine learning.