Editorial Exploratory research in machine learning

Dietterich, Thomas G.

doi:10.1007/bf00115892

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…The work in this thesis falls into the category of exploratory research (Dietterich, 1990) in robot learning -existing approaches to tool and action learning are unable to solve the tool-learning problem presented here. Perhaps not surprisingly, there is as yet no set of standard learning problems for tool using agents.…”

Section: Evaluation Methods and Learning Tasksmentioning

confidence: 99%

A Relational Approach to Tool-Use Learning in Robots

Brown

Sammut

2013

Inductive Logic Programming

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International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.' Solly Brown Authenticity Statement 'I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.'

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Section: Evaluation Methods and Learning Tasksmentioning

confidence: 99%

A Relational Approach to Tool-Use Learning in Robots

Brown

Sammut

2013

Inductive Logic Programming

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“…No amount of mathematical analysis can therefore determine whether a learning algorithm will work well. Experimental studies are therefore required [24]. This paper proposes to employ an experimental study in the application of machine learning to solve this 'bottleneck' problem by demonstrating how a computer system can be used to make intelligent decisions about a user's health.…”

Section: B Model-based Systemsmentioning

confidence: 98%

Intelligent Automated Health Systems for Compliance Monitoring

Diegel

2005

TENCON 2005 - 2005 IEEE Region 10 Conference

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Intelligent health systems integrate users and technology. Elderly users have very specific requirements for health and safety. They constitute a special user group as they are strongly habit focused and are thus good subjects for intelligent systems. Smart house computer management systems need to have intelligent systems in order to make users lifestyles safe and healthy while, at the same time, giving them independence of living. This paper describes how such a system was implemented whereby the computer incorporates an intelligent system which initially learns from its user's habits and then starts to make intelligent decisions based on these patterns.

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“…This paper takes the form of an exploratory research report in the sense described by Dietterich (1990). Following his advice, we state clearly a promising new problem for machine learning and explore its various facets.…”

mentioning

confidence: 99%

Inductive process modeling

et al. 2007

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In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such notations are commonly used. We also argue that existing induction methods are not well suited to this task, although some techniques hold partial solutions. In response, we describe an approach to learning process models from time-series data and illustrate its behavior in three domains. In closing, we describe open issues in process model induction and encourage other researchers to tackle this important problem.

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Editorial Exploratory research in machine learning

Cited by 21 publications

References 18 publications

A Relational Approach to Tool-Use Learning in Robots

A Relational Approach to Tool-Use Learning in Robots

Intelligent Automated Health Systems for Compliance Monitoring

Inductive process modeling

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