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

How to Do Things With Words

We may not be able to make you love reading, but archaeology of knowledge will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.

The archaeology of knowledge

The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society

Truth and method

Genre theory has in recent years entered the study of information (Andersen, 2008). The question remains whether we in the information field have made our own independent contributions to genre theory, not only ‘applying’ genre theory. From 6 different positions, this workshop will discuss the role and potential of genre studies in the study of information and what the study of information can contribute with to the study of genre. Coming from universities in North America and Denmark, the panelists will each present a perspective or argument. Each perspective or argument is developed around the question what the study of information can contribute with to the study of genre.

What Can the Study of Information Do to Genre Studies

The main line of argument to be pursued is that a theory of knowledge organization must be stronger connected to a theory of documents. Documents are infonnation channels fonned by technological innovations, cultural and/or scientific nonns and domain specific needs. This must be taken into account in knowledge organization, but it demands an understanding of the production and use of documents in various social contexts.

Jack Andersen

Papers

What Can the Study of Information Do to Genre Studies

Document Theory and Knowledge Organization. An Approach based on Epistemology and Sociology of Knowledge

Book review: Information, knowledge, text