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

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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The FAIR Guiding Principles for scientific data management and stewardship

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

物件導向軟體之架構(Object-Oriented Software Construction)探討

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

This paper reports the results of a project to develop an interactive visualization artifact to view data from a utility market as a natural scene. The visualization was the product of a series of experiments and prototypes, with the final design bearing structural resemblance to a decorated tree. It presents the raw data and its patterns of change within an integrated structure consisting of color-coded graphs, layers of pie charts, iconic markers, and parallel coordinates, while retaining overall comprehensibility. This paper details process based on the methodology or Robertson, a prototype implementation illustrated with an example walk through, and we comment on the merit of this approach.

Natural scene representation for visualizing high-dimensional data in a large-scale time-critical environment

The design of the myExperiment social web site for scientists adopted the Web 2.0 design principles, investigating the question: does Web 2.0 work for scientists? Two years later the site has thousands of users and the largest public collection of scientific workflows of its kind. Here we reflect on two of the Web 2.0 design principles and the insights we have gained into Web 2.0 for researchers. They reveal new forms of sharable object and new ways of assembling data and services.

Lessons from myExperiment: Two insights into emerging e-Research practice

The scientific paradigms of the Semantic Web, Web Services,
Agents, Peer-to-Peer Networks and Grid Computing are currently
receiving a lot of attention in the research community, and are
producing solutions to important problems ranging from e-science
to e-business. The United States DAML program, the European
Commission and other organisations have also been investing
heavily in these technologies. This Dagstuhl Seminar brought
together world-leading experts from the diverse organizations and
research areas. It strengthened the international collaboration
with the aim to realize the vision of the Semantic Grid.

05271 - Report on the Dagstuhl Seminar on Semantic Grid - Convergence of Technologies

From 03.07.05 to 08.07.05, the Dagstuhl Seminar 05271 ``Semantic Grid -- The Convergence of Technologies'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.

Carole Goble

Papers

Natural scene representation for visualizing high-dimensional data in a large-scale time-critical environment

Lessons from myExperiment: Two insights into emerging e-Research practice

05271 - Report on the Dagstuhl Seminar on Semantic Grid - Convergence of Technologies

05271 Abstracts Collection -- Semantic Grid: The Convergence of Technologies

Proceedings of the International Cross-Disciplinary Workshop on Web Accessibility, Chiba, Japan, May 10-14, 2005