Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/61787
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | de Oliveira, Caterine Silva | en |
dc.contributor.author | Sanin, Cesar | en |
dc.contributor.author | Szczerbicki, Edward | en |
dc.date.accessioned | 2024-07-24T06:35:02Z | - |
dc.date.available | 2024-07-24T06:35:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Procedia Computer Science, v.176, p. 3093-3102 | en |
dc.identifier.issn | 1877-0509 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/61787 | - |
dc.description.abstract | <p>Current computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning, combined with cognitive abilities that can broadly generalize to many tasks. However, in practice, perceiving the environment and adapting to unforeseen changes remains elusive, especially for real time applications that has to deal with high-dimensional data processing with strictly low latency. The challenge is not only to extract meaningful information from this data, but to gain knowledge and also to discover insight to optimize the performance of the system. We envision to tackle these difficulties by bringing together the best of machine learning and human cognitive capabilities in a collaborative way. For that, we propose an approach based on a combination of Human-in-the-Loop and Knowledge Discovery in which feedback is used to discover knowledge by enabling users to interactively explore and identify useful information so the system can be continuously trained to gain previously unknown knowledge and also generate new insights to improve human decisions.</p> | en |
dc.language | en | en |
dc.publisher | Elsevier BV | en |
dc.relation.ispartof | Procedia Computer Science | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization | en |
dc.type | Conference Publication | en |
dc.relation.conference | KES 2020: 24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES) Conference | en |
dc.identifier.doi | 10.1016/j.procs.2020.09.179 | en |
dcterms.accessRights | UNE Green | en |
local.contributor.firstname | Caterine Silva | en |
local.contributor.firstname | Cesar | en |
local.contributor.firstname | Edward | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | cmaldon3@une.edu.au | en |
local.output.category | E1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.date.conference | 16th to 18th of September, 2020 | en |
local.conference.place | Virtual Conference | en |
local.publisher.place | The Netherlands | en |
local.format.startpage | 3093 | en |
local.format.endpage | 3102 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 176 | en |
local.title.subtitle | Towards Cognitive Systems Optimization | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | de Oliveira | en |
local.contributor.lastname | Sanin | en |
local.contributor.lastname | Szczerbicki | en |
dc.identifier.staff | une-id:cmaldon3 | en |
local.profile.orcid | 0000-0001-8515-417X | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/61787 | en |
local.date.onlineversion | 2020-10-02 | - |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Human Feedback and Knowledge Discovery | en |
local.output.categorydescription | E1 Refereed Scholarly Conference Publication | en |
local.conference.details | KES 2020: 24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES) Conference, Virtual Conference, 16th to 18th of September, 2020 | en |
local.search.author | de Oliveira, Caterine Silva | en |
local.search.author | Sanin, Cesar | en |
local.search.author | Szczerbicki, Edward | en |
local.open.fileurl | https://rune.une.edu.au/web/retrieve/217eec40-b424-4664-a0f8-f2aa6c4c4ac7 | en |
local.uneassociation | No | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.available | 2020 | en |
local.year.published | 2020 | en |
local.fileurl.open | https://rune.une.edu.au/web/retrieve/217eec40-b424-4664-a0f8-f2aa6c4c4ac7 | en |
local.fileurl.openpublished | https://rune.une.edu.au/web/retrieve/217eec40-b424-4664-a0f8-f2aa6c4c4ac7 | en |
local.subject.for2020 | 4602 Artificial intelligence | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.date.moved | 2024-08-05 | en |
Appears in Collections: | Conference Publication School of Science and Technology |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
openpublished/HumanOliveira2020ConferencePublication.pdf | Published Version | 1.03 MB | Adobe PDF Download Adobe | View/Open |
SCOPUSTM
Citations
2
checked on Nov 23, 2024
This item is licensed under a Creative Commons License