Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often use their visual perception and other implicit prior knowledge or expertise without any obvious constraints to search for the significant features, whereas machines are limited to a pre-programmed set of criteria to work with; (2) in a team collaboration of collective problem solving, the human experts have diverse abilities that are complementary, and they learn from each other to succeed in cognitively complex tasks in ways that are still impossible imitate by machines.
Original languageUnknown
Title of host publicationProceedings of WORLDCOMP'10
Pages202-2010
Publication statusPublished - 1 Jan 2010
EventWorld Congress in Computer Science, Computer Engineering, and Applied Computing -
Duration: 1 Jan 2010 → …

Conference

ConferenceWorld Congress in Computer Science, Computer Engineering, and Applied Computing
Period1/01/10 → …

Cite this

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title = "Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles",
abstract = "The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often use their visual perception and other implicit prior knowledge or expertise without any obvious constraints to search for the significant features, whereas machines are limited to a pre-programmed set of criteria to work with; (2) in a team collaboration of collective problem solving, the human experts have diverse abilities that are complementary, and they learn from each other to succeed in cognitively complex tasks in ways that are still impossible imitate by machines.",
author = "{DEE Group Author} and Ribeiro, {Maria Rita Sarmento de Almeida}",
year = "2010",
month = "1",
day = "1",
language = "Unknown",
pages = "202--2010",
booktitle = "Proceedings of WORLDCOMP'10",

}

DEE Group Author & Ribeiro, MRSDA 2010, Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles. in Proceedings of WORLDCOMP'10. pp. 202-2010, World Congress in Computer Science, Computer Engineering, and Applied Computing, 1/01/10.

Pattern Recognition in Collective Cognitive Systems: Hybrid Human-Machine Learning (HHML) By Heterogeneous Ensembles. / DEE Group Author ; Ribeiro, Maria Rita Sarmento de Almeida.

Proceedings of WORLDCOMP'10. 2010. p. 202-2010.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

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N2 - The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often use their visual perception and other implicit prior knowledge or expertise without any obvious constraints to search for the significant features, whereas machines are limited to a pre-programmed set of criteria to work with; (2) in a team collaboration of collective problem solving, the human experts have diverse abilities that are complementary, and they learn from each other to succeed in cognitively complex tasks in ways that are still impossible imitate by machines.

AB - The ubiquitous role of the cyber-infrastructures, such as the WWW, provides myriad opportunities for machine learning and its broad spectrum of application domains taking advantage of digital communication. Pattern classification and feature extraction are among the first applications of machine learning that have received extensive attention. The most remarkable achievements have addressed data sets of moderate-to-large size. The 'data deluge' in the last decade or two has posed new challenges for AI researchers to design new, effective and accurate algorithms for similar tasks using ultra-massive data sets and complex (natural or synthetic) dynamical systems. We propose a novel principled approach to feature extraction in hybrid architectures comprised of humans and machines in networked communication, who collaborate to solve a pre-assigned pattern recognition (feature extraction) task. There are two practical considerations addressed below: (1) Human experts, such as plant biologists or astronomers, often use their visual perception and other implicit prior knowledge or expertise without any obvious constraints to search for the significant features, whereas machines are limited to a pre-programmed set of criteria to work with; (2) in a team collaboration of collective problem solving, the human experts have diverse abilities that are complementary, and they learn from each other to succeed in cognitively complex tasks in ways that are still impossible imitate by machines.

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