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

การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Emotion and Adaptation

The measurement of meaning

An Introduction to MultiAgent Systems.

Within the AGM paradigm revision and contraction operators are constrained by a set of rationality postulates. The logical properties of a set of knowledge are not strong enough to uniquely determine a revision or contraction operation, therefore constructions for these operators rely on some form of underlying preference relation, such as a systems of spheres, or an epistemic entrenchment ordering. The problem of iterated revision is determining how the underlying preference relation should change in response to the acceptance or contraction of information. We call this process a transmutation. Generalizing Spohn’s approach we define a transmutation of a well-ordered system of spheres using ordinal conditional functions. Similarly, we define the transmutation of a well-ordered epistemic entrenchment using ordinal epistemic entrenchment functions. We provide several conditions which capture the relationship between an ordinal conditional function and an ordinal epistemic entrenchment function, and their corresponding transmutations. These conditions allow an ordinal epistemic entrenchment function to be explicitly constructed from an ordinal conditional function, and vice versa, in such a way that the epistemic state and its dynamic properties are preserved.

Transmutations of Knowledge Systems

Localization of insulin-like growth factor-1 mRNA in murine central nervous system during postnatal development.

The AGM paradigm is a formal approach to ideal and rational information change From a practical perspective it suffers from two shortcomings, the first involves difficulties with respect to the finite representation of information, and the second involves the lack of support for the iteration of change operators. In this paper we show that these practical problems can be solved in theoretically satisfying ways wholely with in the AGM paradigm.

We introduce a partial entrenchment ranking which serves as a canonical representation for a theory base and a well-ranked episterruc entrenchment, and we provide a computational model for adjusting partial entrenchment rankings when they receive new information using a procedure based on the principle of minimal change.

The connection between the standard AGM theory change operators and the theory base change operators developed herein suggest that the proposed computational model for iterated theory base change exhibits desirable behaviour.

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Iterated theory base change: a computational model

Understanding the process of categorization is a primary research goal in artificial intelligence. The conceptual space framework provides a flexible approach to modeling context-sensitive categorization via a geometrical representation designed for modeling and managing concepts. In this paper we show how algorithms developed in computational geometry, and the Region Connection Calculus can be used to model important aspects of categorization in conceptual spaces. In particular, we demonstrate the feasibility of using existing geometric algorithms to build and manage categories in conceptual spaces, and we show how the Region Connection Calculus can be used to reason about categories and other conceptual regions.

https://www.cs.nyu.edu/faculty/davise/commonsense01/final/Gardenfors.pdf

Mary-Anne Williams

Papers

Transmutations of Knowledge Systems

Localization of insulin-like growth factor-1 mRNA in murine central nervous system during postnatal development.

Iterated theory base change: a computational model

Reasoning about categories in conceptual spaces

Weakening conflicting information for iterated revision and knowledge integration