by Francis Galton (2005)
Galton founded the science of Eugenics and coined the word in 1883. He investigated the families of great men and thought genius was hereditary.
Frederic Lawrence Holmes
"This fascinating book is an investigation of scientific creativity. Following the research pathways of outstanding scientists over the past three centuries, it finds common features in their careers and their landmark discoveries and sheds light on the nature of long-term experimental research."--Jacket.
M. Vidyasagar
Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? How much training is required to achieve a specified level of accuracy in the prediction? How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics. This second edition extends and improves upon this material, covering new areas including: Support vector machines. Fat-shattering dimensions and applications to neural network learning. Learning with dependent samples generated by a beta-mixing process. Connections between system identification and learning theory. Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm. Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added. Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist.
Erich Gamma [and others
Four software designers present a catalog of simple and succinct solutions to commonly occurring design problems, using Smalltalk and C++ in example code. These 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves. The authors begin by describing what patterns are and how they can help you design object-oriented software. They go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems.
N. Balakrishnan
"Presenting a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in the earth and atmospheric sciences. Containing contributions from over 100 leading academics, researchers, and practitioners in these fields, this comprehensive book unveils modern, approaches to the acquisition and analysis of data across diverse subject areas like geology, agriculture, animal science, and geophysics. It discusses techniques related to survey methodology, computational statistics, and operations research and covers new areas of interest like non-linear weather forecasting, construction geological surveys, and water pollution assessment"-- "Provides a realistic alternative to the individual user who would like a quick reference containing encyclopedic information that pertains to their particular research interests and needs"--