An Overview of Machine Learning

Carbonell, Jaime G.; Michalski, Ryszard S.; Mitchell, Tom M.

doi:10.1007/978-3-662-12405-5_1

Cited by 225 publications

(129 citation statements)

References 14 publications

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“…In this approach, machine learns whenever it changes its structure, program, or data based on inputs or in response to external information in such a manner that its overall performance is expected to improve [14][15] [16]. In production systems in particular, these methods have been proven to be especially useful for monitoring/diagnosis [17][18] [19] and control [20].…”

Section: Survey Of the State Of The Artmentioning

confidence: 99%

Context extraction for self-learning production systems

Scholze

Stokić

Barata³

et al. 2012

IEEE 10th International Conference on Industrial Informatics

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A new approach for the realisation of self-learning production systems based on a context aware approach, allowing self-adaptation of flexible manufacturing processes in production systems, is presented. The usage of dynamically changing context for adaptation of flexible manufacturing lines/processes is proposed. The presented solution includes services for context extraction, adaptation and self-learning allowing high adaptation of production systems depending on the identified context. A generic architecture following Service Oriented Principles is presented allowing for integration of the proposed solution into various production systems. A successful application of the developed solution in real world manufacturing environment is presented.

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Section: Survey Of the State Of The Artmentioning

confidence: 99%

Context extraction for self-learning production systems

Scholze

Stokić

Barata³

et al. 2012

IEEE 10th International Conference on Industrial Informatics

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“…the students are learning from the ITS. There is a considerable school of thought that there are two basic forms of learning, especially for mathematical domains: knowledge acquisition and skill refinement (Carbonell et al, 1983). The goal of knowledge (skill) acquisition is to get the student to learn new symbolic information coupled with the ability to apply that information in an effective manner.…”

Section: Design Details Of Tutoring Strategymentioning

confidence: 99%

“…plain facts). Hence it can be argued that this sub-tutoring strategy supports many forms of learning (Carbonell et al, 1983): learning by being told (concepts/tasks definition), rote learning (facts) and inductive learning from examples. In pseudo-code terms, this recursive sub-strategy is expressed as follows:…”

Section: A Concept Definition Should Be Prepared For Each Concept/skimentioning

confidence: 99%

User modelling and user adapted interaction in an intelligent tutoring system

Nwana

1991

User Model User-Adap Inter

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User modelling and user-adapted interaction are crucial to the provision of true individualised instruction, which intelligent tutoring systems strive to achieve. This paper presents how user (student) modelling and student adapted instruction is achieved in FITS, an intelligent tutoring system for the fractions domain. Some researchers have begun questioning both the need for detailed student models as well as the pragmatic possibility of building them. The key contributions of this paper are in its attempt to rehabilitate student modelling/adaptive tutoring within ITSs and in FITS's practical use of simple techniques to realise them with seemingly encouraging results; some illustrations are given to demonstrate the latter.

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“…Most graph properties of intrinsic interest to graph theorists are probably not only recursively enumerable, but also recursive. The criteria for discovery established earlier in this paper focus on properties that people actually study in the course of research in graph theory (what [17] calls "natural classes"), not properties that they could study if they were so inclined. Is every graph property of interest to mathematicians expressible in some R-language?…”

Section: Is Every Graph Property Of Interest To Mathematicians Recursmentioning

confidence: 99%

Knowledge representation for mathematical discovery: Three experiments in graph theory

Epstein

Sridharan

1991

Appl Intell

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This paper describes the nature of mathematical discovery (including concept definition and exploration, example generation, and theorem conjecture and proof), and considers how such an intelligent process can be simulated by a machine. Although the material is drawn primarily from graph theory, the results are immediately relevant to research in mathematical discovery and learning.The thought experiment, a protocol paradigm for the empirical study of mathematical discovery, highlights behavioral objectives for machine simulation. This thought experiment provides an insightful account of the discovery process, motivates a framework for describing mathematical knowledge in terms of object classes, and is a rich source of advice on the design of a system to perform discovery in graph theory. The evaluation criteria for a discovery system, it is argued, must include both a set of behavior to display (behavioral objectives) and a target set of facts to be discovered (factual knowledge).Cues from the thought experiment are used to formulate two hierarchies of representational languages for graph theory. The first hierarchy is based on the superficial terminology and knowledge of the thought experiment. Generated by formal grammars with set-theoretic semantics, this eminently reasonable approach ultimately fails to meet the factual knowledge criteria. The second hierarchy uses declarative expressions, each of which has a semantic interpretation as a stylized, recursive algorithm that defines a class by generating it correctly and completely. A simple version of one such representation is validated by a successful, implemented system called Graph Theorist (GT) for mathematical research in graph theory. GT generates correct examples, defines and explores new graph theory properties, and conjectures and proves theorems.Several themes run through this paper. The first is the dual goals, behavioral objectives and factual knowledge to be discovered, and the multiplicity of their demands on a discovery system. The second theme is the central role of object classes to knowledge representation. The third is the increased power and flexibility of a constructive (generator) definition over the more traditional predicate (tester) definition. The final theme is the importance of examples and recursion in mathematical knowledge. The results provide important guidance for further research in the simulation of mathematical discovery.As an ill-structured problem, discovery is potentially the most difficult subfield of machine learning. Successful discovery requires the representation and manipulation of quantities of information, the efficient storage and retrieval of that information, and a processing strategy that focuses upon relevant segments of that information and pursues it with resources appropriate to the information's significance. This paper explores the origin and nature of knowledge repre-

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An Overview of Machine Learning

Cited by 225 publications

References 14 publications

Context extraction for self-learning production systems

Context extraction for self-learning production systems

User modelling and user adapted interaction in an intelligent tutoring system

Knowledge representation for mathematical discovery: Three experiments in graph theory

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