One of the largest and most active areas of AI, machine learning is of interest to students of psychology, philosophy of science, and education. Although self-contained, volume III follows the tradition of volume I (1983) and volume II (1986). Annotation copyrighted by Book News, Inc., Portland, OR

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems-one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

Up to now there has been no scientific publication on natural language research that presents a broad and complex description of the current problems of parsing in the context of Artificial Intelligence. However, there are many interesting results from this domain appearing mainly in numerous articles published in professional journals. In view of this situation, the objective of this book is to enable scientists from different countries to present the results of their research on natural language parsing in the form of more detailed papers than would be possible in professional journals. This book thus provides a collection of studies written by well-known scientists whose earlier publications have greatly contributed to the development of research on natural language parsing.

Abstract: "We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target functions sampled from the prior. We specifically derive upper and lower bounds on the optimal rates under a smoothness condition on the correct prior, with the number of samples per data set equal the VC dimension. These results have implications for the improvements achievable via transfer learning."

Abstract: "Support Vector Machines have received extensive attention in machine learning community and have been successfully applied in pattern recognition and regression problems. Recently, it has also been proposed to solve novelty detection problems, whose objective is to detect novel objects from existing instances. New Event Detection (NED), which can be treated as one special application of novelty detection, has been a research topic in Topic Detection and Tracking (TDT) community for several years. However, the winning technology of NED in the TDT community has remained to be the nearest neighbor method with suitable distance metric in the document vector space. In this paper we investigated Support Vector Machines and kernel regression (as a smoothed nearest neighbor method) for the NED task, and compared them to the nearest neighbor method. We conducted a set of experiments on TDT benchmark collections, and provided analysis on the failure of SVM for not being able to capture Misses."