Product Description
The World Wide Web is rapidly filling with more text than anyone could have imagined a short time ago. However, the task of determining which data is relevant has become appreciably harder. In this original new work Richard Belew brings a cognitive science perspective to the study of information as a computer science discipline. He introduces the idea of Finding Out About (FOA), the process of actively seeking out information relevant to a topic of interest. Belew describes all facets of FOA, ranging from creating a good characterization of what the user seeks to evaluating the successful performance of search engines. His volume clearly shows how to build many of the tools that are useful for searching collections of text and other media. While computer scientists make up the book's primary audience, Belew skillfully presents technical details in a manner that makes important themes accessible to readers more comfortable with words than equations. Resources are available from the book's web site ... Read more

Customer Reviews (5)

Worth having
I found this to be a wonderful book about Search Engines. If you like history, math, computer science, and how we can make computers communicate effectively with people, then this book is a great read. With current Search Engines, we enter some words and get back a bibliography. I look forward to a sequel: "Answer Engines: How You Can Enter a Short, Simple Question and Get Back a Short, Simple Answer (NOT a list of things to read)!"

A refreshing alternative perspective...
I wonder if I was reading the same book as a couple of the reviewers!

IMHO there are (too) many books in this field that deal with how search engines work, but the more 'slippery' concepts associated with how people actually use them to find about information have received much less attention.

The book provides a very welcome cognitive science perspective on a discipline often dominated by those who seek overly simple mechanistic formulas and 'answers' to very tricky 'questions'.

Did the author just discover word processing?
This book, aside from being horribly written, full of awful grammar and confused writing, everything being derivative, etc. (see the next review), suffers from over-stylization. The author seems to have just discovered the bold, italic and underline buttons of his word processor and does not hesitate to use them, along with quotation marks, all caps and margin notes, almost interchangeably. This makes the book very hard to read because your eyes are going from one... styled word or phrase to another.

Please, don't buy this book or use it for your course.

Composition of blocks from works of other authors
Bunch of blocks and citations from other authors with absolutely no explanations. It doesnt look like author understands anything that he borrowed from others to compose this book or dare to give one calculation example or derive a formula. Horrible, horrible book for a novice.

This is the search engine Bible.
Though I have not Taken Dr. Belew's class, I know he is a consumate professional in his field. His Ideas are new and revolutionary. There are a lot of concepts in the book that I am still trying to grasp, but Belew really does a good job of explaining new concepts in a comprehensive way. He is really an asset to the Computer Science community. ... Read more

Product Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
We investigate the problem of scheduling N jobs on parallel machines in L successive stages with limited buffer capacities between stages. The primary objective is to find a schedule that would minimize the makespan. This problem is shown to be NP-hard in the strong sense. We develop a tabu search algorithm for this problem in which the search is limited to the space of permutation vectors of size N. This vector represents the order in which the given set of jobs are performed in the first stage, and we propose a procedure to construct a complete schedule associated with every permutation vector. We conduct an extensive computational experiment using randomly generated instances with different structures and different buffer capacities. We also solve several specific instances of the problem from the open literature. All empirical evidence suggest that the proposed algorithm is an effective method for solving this problem. ... Read more

Product Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This paper discusses the problem of simultaneously selecting and scheduling parallel machines to minimize the sum of machine holding cost and job tardiness cost. A combinatorial optimization model is developed for this purpose. Solving the developed model is NP-hard. A heuristic algorithm is developed to locate the optimal or near optimal solutions based on a Tabu search mechanism specially designed to control the search process in the solution neighborhood for jobs scheduled on specific machines. Numerical examples show that the solutions of the model lead to compromises between the system cost related to machine selection and the operational cost related to job tardiness penalties. The examples also show that the developed algorithm is effective and computationally efficient. ... Read more

Product Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
A methodology for minimizing the weighted tardiness of jobs in unrelated parallel machining scheduling with sequence-dependent setups is presented in this paper. To comply with industrial situations, the dynamic release of jobs and dynamic availability of machines are assumed. Recognizing the inherent difficulty in solving industrial-size problems efficiently, six different search algorithms based on tabu search are developed to identify the best schedule that gives the minimum weighted tardiness. To enhance both the efficiency and efficacy of the search algorithms, four different initial solution finding mechanisms, based on dispatching rules, are developed. While there is no evidence of identifying solutions of better quality by employing a specific initial solution finding mechanism, the use of a specific search algorithm led to identifying solutions of better quality or that required lower computation time, but not both. Based on the extensive statistical analysis performed, the search algorithm with short-term memory and fixed tabu list size is recommended for solving small size problems, while that with long-term memory and minimum frequency for solving medium and large size problems, combined with fixed tabu list size for the former and variable tabu list size for the latter. ... Read more

Product Description

Reactive Search integrates sub-symbolic machine learning techniques into search heuristics for solving complex optimization problems. By automatically adjusting the working parameters, a reactive search self-tunes and adapts, effectively learning by doing until a solution is found. Intelligent Optimization, a superset of Reactive Search, concerns online and off-line schemes based on the use of memory, adaptation, incremental development of models, experimental algorithms applied to optimization, intelligent tuning and design of heuristics.

Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.

Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities and schemes for the automated tuning of these parameters. Anyone working in decision making in business, engineering, economics or science will find a wealth of information here.

Product Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
In this paper we consider the problem of scheduling n preemptive jobs on m machines with identical speed under machine availability and eligibility constraints when minimizing maximum lateness (L"m"a"x). The lateness of a job is defined to be its completion time minus its due date, and L"m"a"x is the maximum value of lateness among all jobs. We assume that each machine is not continuously available at all time throughout the planning horizon and each job is only allowed to be processed on specific machines. Network flow technique is used to formulate this scheduling problem into a series of maximum flow problems. We propose a polynomial time two-phase binary search algorithm to verify the feasibility of the problem and to solve the scheduling problem optimally if a feasible schedule exists. Finally, we show that the time complexity of the algorithm is O((n+(2n+2x))^3log(UB-LB)). Most literature in parallel machine scheduling assume that all machines are continuously available for processing and all jobs can be processed at any available machine throughout the planning horizon. But both assumptions might not be true in some practical environment, such as machine preventive maintenance and machines that have different capabilities to process jobs. This type of scheduling problem is seldom studied in the literature. The purpose of this paper is to examine the scheduling problem with machines with identical speed under machine availability and eligibility constraints. The objective is to minimize maximum lateness. We formulate this scheduling problem into a series of maximum flow problems with different values of L"m"a"x. A polynomial time two-phase binary search algorithm is proposed to verify the feasibility of the problem and to determine the optimal L"m"a"x. ... Read more

Product Description
This digital document is a journal article from Information Processing and Management, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been proposed mainly for title extraction from research papers. It has not been clear whether it could be possible to conduct automatic title extraction from general documents. As a case study, we consider extraction from Office including Word and PowerPoint. In our approach, we annotate titles in sample documents (for Word and PowerPoint, respectively) and take them as training data, train machine learning models, and perform title extraction using the trained models. Our method is unique in that we mainly utilize formatting information such as font size as features in the models. It turns out that the use of formatting information can lead to quite accurate extraction from general documents. Precision and recall for title extraction from Word are 0.810 and 0.837, respectively, and precision and recall for title extraction from PowerPoint are 0.875 and 0.895, respectively in an experiment on intranet data. Other important new findings in this work include that we can train models in one domain and apply them to other domains, and more surprisingly we can even train models in one language and apply them to other languages. Moreover, we can significantly improve search ranking results in document retrieval by using the extracted titles. ... Read more