There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

世界経済・社会統計 = World development indicators

A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. Further experiments compared CFS with a wrapper—a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets. Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates iii feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

/pdf/correlation-based-feature-selection-for-machine-learning-578rirmvg5.pdf

Correlation-based Feature Selection for Machine Learning

We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

/pdf/image-retrieval-ideas-influences-and-trends-of-the-new-age-51whw4w7su.pdf

Image retrieval: Ideas, influences, and trends of the new age

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

/pdf/context-aware-recommender-systems-8uash1fwm4.pdf

Context-aware recommender systems

From the Publisher:
Given modern society's need to control its ever-increasing body of information, digital libraries will be among the most important and influential institutions of this century. With their versatility, accessibility, and economy, these focused collections of everything digital are fast becoming the "banks" in which the world's wealth of information is stored.
How to Build a Digital Library is the only book that offers all the knowledge and tools needed to construct and maintain a digital library-no matter how large or small. Two internationally recognized experts provide a fully developed, step-by-step method, as well as the software that makes it all possible. How to Build a Digital Library is the perfectly self-contained resource for individuals, agencies, and institutions wishing to put this powerful tool to work in their burgeoning information treasuries. 
Features
Sketches the history of libraries-both traditional and digital-and their impact on present practices and future directions Offers in-depth coverage of today's practical standards used to represent and store information digitally Uses Greenstone, freely accessible open-source software-available with interfaces in the world's major languages (including Spanish, Chinese, and Arabic) Written for both technical and non-technical audiences Web-enhanced with software documentation, color illustrations, full-text index, source code, and more

Author Biography: Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann. 
David Bainbridge is a senior lecturer in Computer Science at the University of Waikato, New Zealand. He holds a PhD in Optical Music Recognition from the University of Canterbury, New Zealand where he studied as a Commonwealth Scholar. Since moving to Waikato in 1996 he has continued to broadened his interest in digital media, while retaining a particular emphasis on music. An active member of the New Zealand Digital Library project, he manages the group's digital music library, Meldex, and has collaborated with several United Nations Agencies, the BBC and various public libraries. David has also worked as a research engineer for Thorn EMI in the area of photo-realistic imaging and graduated from the University of Edinburgh in 1991 as the class medalist in Computer Science.

How to Build a Digital Library

The New Zealand Digital Library MELody inDEX

This article describes the challenges posed by optical music recognition - a topic in com- puter science that aims to convert scanned pages of music into an on-line format. First, the problem is described; then a generalised framework for software is presented that emphasises key stages that must be solved: staff line identification, musical object location, musical feature classification, and musical semantics. Next, significant research projects in the area are reviewed, showing how each fits the generalised framework. The article concludes by discussing perhaps the most open question in the field: how to compare the accuracy and success of rival systems, highlighting certain steps that help ease the task.

The Challenge of Optical Music Recognition

This paper describes the Greenstone digital library software, a comprehensive, open-source system for the construction and presentation of information collections. Collections built with Greenstone offer effective full-text searching and metadata-based browsing facilities that are attractive and easy to use. Moreover, they are easily maintainable and can be augmented and rebuilt entirely automatically. The system is extensible: software "plugins" accommodate different document and metadata types.

/pdf/greenstone-a-comprehensive-open-source-digital-library-1retqz4m1c.pdf

Greenstone: a comprehensive open-source digital library software system

Greenstone is a suite of software for building and distributing digital library collections. It provides a new way of organizing information and publishing it on the Internet or on CD-ROM. Greenstone is produced by the New Zealand Digital Library Project at the University of Waikato, and developed and distributed in cooperation with UNESCO and the Human Info NGO. It is open-source software, available from http://greenstone.org under the terms of the GNU General Public License.

David Bainbridge

Papers

How to Build a Digital Library

The New Zealand Digital Library MELody inDEX

The Challenge of Optical Music Recognition

Greenstone: a comprehensive open-source digital library software system

Developer's guide