Constructing Kernel Machines in the Empirical Kernel Feature Space

Reducing the Complexity of Kernel Machines with Neural Growing Gas in Feature Space

Advances in Artificial Intelligence – IBERAMIA 2004 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-30498-2_80 ◽

2004 ◽

pp. 799-808 ◽

Cited By ~ 7

Author(s):

Luigina D’Amato ◽

José Ali Moreno ◽

Rosa Mujica

Keyword(s):

Feature Space ◽

Kernel Machines

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The Utility of an Intermediate Representation of Feature Space: Lessons From Fingerprint Examiners

PsycEXTRA Dataset ◽

10.1037/e502412013-026 ◽

2012 ◽

Author(s):

Tom Busey ◽

Chen Yu ◽

Francisco Parada ◽

Brandi Emerick ◽

John Vanderkolk

Keyword(s):

Feature Space ◽

Intermediate Representation

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Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽

10.1609/aimag.v40i3.4810 ◽

2019 ◽

Vol 40 (3) ◽

pp. 41-57

Author(s):

Manisha Mishra ◽

Pujitha Mannaru ◽

David Sidoti ◽

Adam Bienkowski ◽

Lingyi Zhang ◽

...

Keyword(s):

Decision Support ◽

Situational Awareness ◽

Feature Space ◽

Human Interaction ◽

Superior Performance ◽

Uncertain Environments ◽

Cognitive States ◽

Contextual Elements ◽

Human Operators ◽

Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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