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Illustrated with more than 200 artworks and photographs and accompanied by exhaustive technical specifications, Colt: An American Classic is an expertly written account of the firearms produced by one of the world’s best-known and easily recognized gun manufacturers.
This fascinating visual account of firearms shows everything from the earliest cannons to modern weapons of war. It also highlights how gun technology and military tactics developed in tandem over time. Centuries ago, the Chinese discovered that if they put gunpowder and a projectile into a metal tube and ignited it, they could fire the projectile with enormous force. The first guns were born. Firearms: An Illustrated History showcases over 300 firearms including pistols, revolvers, rifles, shotguns, machine-guns, and artillery, each with annotated close-up photographs and details of their origins, barrel, and caliber. It details the use of the firearms, not just in the military but for sport, hunting, and law enforcement. This comprehensive volume traces the history of firearms, highlighting "turning points" such as the rifle with its parallel spiraled groves that could impart a spin to bullets making them fly straighter. It also showcases iconic firearms such as the Walther PPK self-loading pistol popularised in James Bond films. With information on the great gunsmiths including Beretta and Kalashnikov and a detailed guide to how guns work, Firearms: An Illustrated History is an essential purchase for everyone interested in guns and military history.
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.