Few Americans would disagree that serious problems plague their country's education system. But what is the real reason for this serious malady? According to author and long-time teacher Rick Tobin, the usual suspects-beleaguered teachers, overcrowded classrooms, lack of funds, etc.-aren't to blame. Tobin believes that critics of the educational system have totally missed the point and that educational reform is aimed in the wrong direction. In his view, academic failure is the direct result of a loss of faith in American democracy. Current teaching objectives and the laws governing schools are failing to emphasize moral responsibility and personal accountability-fundamental aspects of the ethical framework that form the basis of American democracy and American education. These problems stem from what Tobin terms a prevailing "egoist" culture in America that largely ignores moral character building and downplays the need for individual accountability. He contends there are millions of Americans who fall within nine "classes" of people who are largely responsible for our educational mediocrity. Rick Tobin doesn't mince words in this candid expose, in which he also offers solutions for remedying our educational woes.

Comprehensive Coverage of the Entire Area of Classification Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial and Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation and implementation, image processing, and engineering applications.