For 18 year old Sam Bladen, the only bright spot in his small town delinquent existence is Danny Eagan, a nerdy, unstable art student. Responding to a dare, Sam meets Brande, a member of the Sans Merci Troupe and self-styled Messiah of Monsters. Under the charismatic freak's tortuous tutelage, the dare soon turns deadly, and Sam begins an agonizing process which leads him to question the very basis of human morality. Will he choose a comfortable relationship with Danny, or will his self-destructive behavior and encounter with the unknown unbalance everything? An unusual mix of graphic sex and cerebral horror, Sam's unique voice is dark, witty, and provocative. Whoever said true love never dies never had to bury the bodies...

An accessible and up-to-date treatment featuring the connection between neural networks and statistics

A Statistical Approach to Neural Networks for Pattern Recognition presents a

statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such as:

How robust is the model to outliers?

Could the model be made more robust?

Which points will have a high leverage?

What are good starting values for the fitting algorithm?

Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature.

Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS(R) codes that are available on the book's related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a criticalreference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.