Nations are made and unmade at their borders, and the forty-ninth parallel separating Montana and Alberta in the late nineteenth century was a pivotal Western site for both the United States and Canada. Blackfoot country was a key site of Canadian and American efforts to shape their nations and national identities. The region?s landscape, aboriginal people, newcomers, railroads, and ongoing cross-border ties all challenged the governments? efforts to create, colonize, and nationalize the Alberta-Montana borderlands. The Line Which Separates makes an important and useful comparison between American and Canadian government policies and attitudes regarding race, gender, and homesteading.øFederal visions of the West in general and the borderlands in particular rested on overlapping sets of assumptions about space, race, and gender; those same assumptions would be used to craft the policies that were supposed to turn national visions into local realities. The growth of a white female population in the region, which should have ?whitened? and ?easternized? the region, merely served to complicate emerging categories. Both governments worked hard to enforce the lines that were supposed to separate "good" land from "bad," whites from aboriginals, different groups of newcomers from each other, and women's roles from men's roles. The lines and categories they depended on were used to distinguish each West, and thus each nation, from the other. Drawing on a range of sources, from government maps and reports to oral testimony and personal papers, The Line Which Separates explores the uneven way in which the borderlands were superimposed on Blackfoot country in order to divide a previously cohesive region in the late nineteenth century.

Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate and postgraduate students of computer science and allied disciplines.