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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

How to ... be a reflective practitioner.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.

/pdf/informed-recommender-basing-recommendations-on-consumer-1man2b75lo.pdf

Informed Recommender: Basing Recommendations on Consumer Product Reviews

FOR MANY PEOPLE AROUND THE GLOBE, the Internet has become the place where they instinctively turn to for all kinds of information, particularly after the Coseil Europeen pour la Recherche Nucleaire (CERN) introduced the World Wide Web (WWW or the Web) in the late 1980s. The Internet has given birth to new research fields or has diversified existing research fields connected with human activities, including computer-mediated communication (CMC), computer-supported cooperative work (CSCW), electronic commerce, virtual communities, virtual architecture, various virtual environments, and information design. The Web phenomenon raised a number of research issues concerning the mechanisms and rules governing Internet activities, particularly the interaction of technology and society. Decision makers at different levels needed knowledge about the phenomenon, so

Complementary Explorative Data Analysis: The Reconciliation of Quantitative and Qualitative Principles

One: Basic Concepts.- One: The Concept of A Virtual Design Studio.- 1.1 Scenario.- 1.2 Experiences.- 1.3 Observations.- 1.4 Directions.- References.- Two: Network Technology.- 2.1 TCP/IP Communication.- 2.2 The Internet.- 2.3 The World Wide Web.- 2.4 Implications.- References.- Three: Digital Design Media.- 3.1 Images.- 3.2 CAD and 3D Models.- 3.3 Text.- 3.4 Hypermedia.- 3.5 Summary.- References.- Two: Communication and Representation.- Four: Communication in a Virtual Environment.- 4.1 Computer Mediated Communication.- 4.2 Communication Tools.- 4.2.1 Asynchronous Tools: Email, List Servers, Bulletin Boards.- Email.- List Servers.- Bulletin Boards.- 4.2.2 Synchronous Tools: Chat, Video Conference, Broadcast.- IRC, ICQ and Chatting Systems.- Video Conference.- Broadcast.- 4.3 Virtual Worlds.- 4.3.1 Communication in a Text-Based VW.- 4.3.2 Navigation in Text-Based VW.- 4.3.3 Actions in a Text-Based VW.- 4.4 Use Of Communication Tools in a VDS.- 4.4.1 Effective Use Of Communication Tools in a VDS.- 4.4.2 Alternative Communication Channels.- 4.5 Summary.- References.- Five: Shared Representation in a Vds.- 5.1. The Roles of Shared Representation.- 5.2. Structuring Shared Representations as Hypermedia.- 5.2.1 Structural Consistency.- 5.2.2 Labels And Information Retrieval.- 5.2.3 Dynamic Versus Static Linking.- 5.3 The Content of Shared Representation.- 5.3.1 Activity/Space Ontology.- 5.3.2 Function/Behaviour/Structure Ontology.- 5.4 Shared Representation - Ontology and Hypermedia.- References.- Three: The Shared Environment.- Six: The Distributed Design Studio.- 6.1 Loosely Coupled Desktop.- 6.1.1 Integration Agreements and Interface Design.- 6.1.2 Management and Collaboration.- 6.1.3 Communication and Collaboration.- 6.1.4 Handling Project Information, Library Support and Documentation.- 6.1.5 Diversity and Discontinuity in Loosely Coupled VDS.- 6.2 The Tightly Integrated VDS Desktop.- 6.2.1 Integration Agreements and Interface Design.- 6.2.2 Management and Collaboration.- 6.2.3 Communication and Collaboration.- 6.2.4 Handling Project Information, Library Support and Documentation.- 6.2.5 Customisation and Further Automation in a Tightly Integrated VDS.- 6.3 Recapitulation.- References.- Seven: A Centralised VDS Environment.- 7.1 The Desktop Metaphor.- 7.1.1 Habanera.- 7.1.2 TeamWave.- 7.2 The Place Metaphor.- 7.2.2 Virtual Realities.- Colony City.- Activeworlds.- The Cave Environment.- 7.2.3 Virtual Worlds.- StudioMOO.- 7.3 Beyond Metaphors.- References.- Epilogue.- Appendix: Web Resources.- A.1 General Information for Virtual Design Studios.- A.2 Communication Resources.- A.3 Virtual Worlds.- Author Information.

Understanding Virtual Design Studios

http://maryloumaher.net/Pubs/2000pdf/JDS_final.pdf

Analysing participation in collaborative design environments

In this paper we describe an e-Tourism environment that takes a community-driven approach to foster a lively society of travelers who exchange travel experiences, recommend tourism destinations or just listen to catch some interesting gossip. Moreover, business transactions such as booking a trip or getting assistance from travel advisors or community members are constituent parts of this environment. All these happen in an integrated, game-like e-Business application where each e-Tourist is impersonated as an avatar. More precisely, we apply 3D Electronic Institutions, a framework developed and employed in the area of multi-agent systems, to the tourism domain. The system interface is realized by means of a 3D game engine that provides sophisticated 3D visualization and enables humans to interact with the environment. We present “itchy feet”, a prototype implementing this 3D e-Tourism environment to showcase first visual impressions. This new environment is a perfect research playground for examining heterogeneous societies comprising humans and software agents, and their relationship in e-Tourism.

/pdf/opening-new-dimensions-for-e-tourism-my15h2bw91.pdf

Simeon J. Simoff

Papers

Informed Recommender: Basing Recommendations on Consumer Product Reviews

Complementary Explorative Data Analysis: The Reconciliation of Quantitative and Qualitative Principles

Understanding Virtual Design Studios

Analysing participation in collaborative design environments

Opening new dimensions for e-Tourism