{"id":"https://openalex.org/W2709496712","doi":"https://doi.org/10.1142/s0218001418510023","title":"A Novel Method Based on Line-Segment Visualizations for Hyper-Parameter Optimization in Deep Networks","display_name":"A Novel Method Based on Line-Segment Visualizations for Hyper-Parameter Optimization in Deep Networks","publication_year":2017,"publication_date":"2017-06-23","ids":{"openalex":"https://openalex.org/W2709496712","doi":"https://doi.org/10.1142/s0218001418510023","mag":"2709496712"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001418510023","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001418510023","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048492871","display_name":"Xue\u2010song Tang","orcid":"https://orcid.org/0000-0002-7594-2241"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xue-song Tang","raw_affiliation_strings":["Department of Information Science, Donghua University, Shanghai 201620, P. R. China"],"affiliations":[{"raw_affiliation_string":"Department of Information Science, Donghua University, Shanghai 201620, P. R. China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100688228","display_name":"Yongsheng Ding","orcid":"https://orcid.org/0000-0001-5631-3581"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongsheng Ding","raw_affiliation_strings":["Department of Information Science, Donghua University, Shanghai 201620, P. R. China"],"affiliations":[{"raw_affiliation_string":"Department of Information Science, Donghua University, Shanghai 201620, P. R. China","institution_ids":["https://openalex.org/I181326427"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001762132","display_name":"Kuangrong Hao","orcid":"https://orcid.org/0000-0001-9672-6161"},"institutions":[{"id":"https://openalex.org/I181326427","display_name":"Donghua University","ror":"https://ror.org/035psfh38","country_code":"CN","type":"education","lineage":["https://openalex.org/I181326427"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuangrong Hao","raw_affiliation_strings":["Department of Information Science, Donghua University, Shanghai 201620, P. R. China"],"affiliations":[{"raw_affiliation_string":"Department of Information Science, Donghua University, Shanghai 201620, P. R. China","institution_ids":["https://openalex.org/I181326427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048492871"],"corresponding_institution_ids":["https://openalex.org/I181326427"],"apc_list":null,"apc_paid":null,"fwci":0.6152,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76006711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"32","issue":"03","first_page":"1851002","last_page":"1851002"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8887463808059692},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.8132808208465576},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7946637868881226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7569978833198547},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.5712964534759521},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4936576783657074},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.46586230397224426},{"id":"https://openalex.org/keywords/line-segment","display_name":"Line segment","score":0.44630688428878784},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4323454201221466},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4291994571685791},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.42881423234939575},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4213939309120178},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4208308458328247},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09981563687324524}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8887463808059692},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8132808208465576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7946637868881226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7569978833198547},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.5712964534759521},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4936576783657074},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46586230397224426},{"id":"https://openalex.org/C182124507","wikidata":"https://www.wikidata.org/wiki/Q166154","display_name":"Line segment","level":2,"score":0.44630688428878784},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4323454201221466},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4291994571685791},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.42881423234939575},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4213939309120178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4208308458328247},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09981563687324524},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001418510023","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001418510023","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2001944419","display_name":null,"funder_award_id":"61473078","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4313126925","display_name":null,"funder_award_id":"61473077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6859854672","display_name":null,"funder_award_id":"61503075","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1910619957","https://openalex.org/W1996187920","https://openalex.org/W2013566947","https://openalex.org/W2025768430","https://openalex.org/W2097998348","https://openalex.org/W2100495367","https://openalex.org/W2100808586","https://openalex.org/W2103901176","https://openalex.org/W2108665656","https://openalex.org/W2127301647","https://openalex.org/W2140619591","https://openalex.org/W2141200610","https://openalex.org/W2160072137","https://openalex.org/W2172014587","https://openalex.org/W2189422931","https://openalex.org/W2194775991","https://openalex.org/W2553798555","https://openalex.org/W2561545477","https://openalex.org/W2787855326"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W4385957992","https://openalex.org/W3129898729","https://openalex.org/W2977498432","https://openalex.org/W4287995534","https://openalex.org/W4229079080","https://openalex.org/W3006943036","https://openalex.org/W4299487748","https://openalex.org/W3208423683","https://openalex.org/W2674501427"],"abstract_inverted_index":{"Recently,":[0],"deep":[1,21,41,73,101],"learning":[2,22,42],"has":[3],"been":[4],"widely":[5],"applied":[6,78],"in":[7],"various":[8],"areas":[9],"and":[10,50,103],"achieved":[11],"remarkable":[12],"research":[13],"findings.":[14],"The":[15,75],"major":[16],"reason":[17],"that":[18,26,59,91],"makes":[19],"the":[20,36,61,69,72,92,97,100,105,108],"paradigm":[23],"successful":[24],"is":[25,77,89],"it":[27],"can":[28,95],"effectively":[29],"learn":[30],"a":[31,56,80],"hierarchical":[32],"feature":[33],"structure":[34],"for":[35,71,107],"training":[37,48],"data.":[38],"However,":[39],"most":[40],"algorithms":[43],"rely":[44],"on":[45],"massive":[46],"well-labeled":[47],"datasets":[49],"hyper-parameter":[51],"configurations.":[52],"This":[53],"paper":[54],"proposed":[55],"novel":[57],"methodology":[58,76],"uses":[60],"geometric":[62],"characteristics":[63],"of":[64,99],"line-segment":[65],"representations":[66],"to":[67,79,84],"optimize":[68],"hyper-parameters":[70],"networks.":[74],"line-segment-based":[81,93],"stacked":[82],"auto-encoder":[83],"verify":[85],"its":[86],"effectiveness.":[87],"It":[88],"found":[90],"visualizations":[94],"increase":[96],"interpretability":[98],"models":[102],"facilitate":[104],"configurations":[106],"hyper-parameters.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}