IBM MQ
Introduction and overview
This Chapter describes the scope of this book and introduces WebSphere
Business Integration Message Broker, WebSphere Business Integration Event
Broker, and their main components.
The source code samples for Chapter 2, 4, 6, and 8 are contained in the
EvenChapters project. Those Chapters all reference various aspects of this single project.
The source code for the BullsEye control (Chapter 10 example) is in the BullEyeCtl project.
The source samples for the other Chapters are provided in the Chapter XX sub-directories.
In this edition, the majority of the book is dedicated to covering the Winsock API. Chapter 1 starts with an introduction to Winsock and is specifically geared for the beginning Winsock programmer. This Chapter covers all the basics and introduces Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) through simple samples, as well as providing a roadmap to advanced Winsock topics covered in other Chapters. For the sake of simplicity, Chapter 1 covers the IPv4 protocol.
The purpose of this Chapter is to present a survey of recent publications concerning medical
image registration techniques. These publications will be classified according to a model based
on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods
一個完整的email客戶端代碼
Example program from Chapter 1 Programming Spiders, Bots and Aggregators in Java Copyright 2001 by Jeff Heaton SendMail is an example of client sockets. This program presents a simple dialog box that prompts the user for information about how to send a mail.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This Chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
