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Adaptive Learning in Macroeconomics
2020While rational expectations (RE) remains the benchmark paradigm in macro-economic modeling, bounded rationality, especially in the form of adaptive learning, has become a mainstream alternative. Under the adaptive learning (AL) approach, economic agents in dynamic, stochastic environments are modeled as adaptive learners forming expectations and making
Bruce McGough, George W. Evans
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1989
There are many anatomical differences between speakers, such as the length of the vocal tract, the size of the nasal cavity, etc.. There are also many speaker-dependent speaking habits, such as accent, speed, loudness, etc.. These differences have been considered so extreme that many researchers have forsaken speaker-independent recognition.
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There are many anatomical differences between speakers, such as the length of the vocal tract, the size of the nasal cavity, etc.. There are also many speaker-dependent speaking habits, such as accent, speed, loudness, etc.. These differences have been considered so extreme that many researchers have forsaken speaker-independent recognition.
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, 2016 Organizational Learning and Adaptation
2017Organizational learning theory is motivated by the observation that organizations learn by encoding inferences from experience into their behavior. It seeks to answer the questions of what kinds of experiences influence behaviors, how and under what circumstances behaviors change, and how new behaviors are stabilized and have consequences for ...
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2005
This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described.
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This chapter demonstrates the use of machine learning techniques in adaptive hypermedia systems. A generic machine learning scenario is described and related to an abstract definition of interactive software systems and adaptive hypermedia systems. In the main part of the chapter, numerous recent systems and employed techniques are described.
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Learning and Domain Adaptation
2009Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises
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