Loading…
Response to Comment on “Quantifying long-term scientific impact”
Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers tha...
Saved in:
Published in: | Science (American Association for the Advancement of Science) 2014-07, Vol.345 (6193), p.149-149 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203 |
---|---|
cites | cdi_FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203 |
container_end_page | 149 |
container_issue | 6193 |
container_start_page | 149 |
container_title | Science (American Association for the Advancement of Science) |
container_volume | 345 |
creator | Wang, Dashun Song, Chaoming Shen, Hua-Wei Barabási, Albert-László |
description | Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach. |
doi_str_mv | 10.1126/science.1248961 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1544396995</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3369066771</sourcerecordid><originalsourceid>FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203</originalsourceid><addsrcrecordid>eNotkM1KAzEUhYMoWKtrtwHX0-ZnJj9LqVqFgii6Dpl4p0zpJGOSLrrrg-jL9Umc2q4unHs4h_MhdEvJhFImpsm14B1MKCuVFvQMjSjRVaEZ4edoRAgXhSKyukRXKa0IGX6aj9DDO6Q--AQ4BzwLXQc-4-DxfvfztrE-t8229Uu8Dn5ZZIgd_q8Z5Nbhtuuty_vd7zW6aOw6wc3pjtHn0-PH7LlYvM5fZveLwjGhcyEry61kXDDtrLUOqhKg1k0poKY1o1DaL-UsUw6sFEwoWkvOFZMKZH3YMUZ3x9w-hu8NpGxWYRP9UGloVZZcC62rwTU9ulwMKUVoTB_bzsatocQcUJkTKnNCxf8AKitf7Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1544396995</pqid></control><display><type>article</type><title>Response to Comment on “Quantifying long-term scientific impact”</title><source>American Association for the Advancement of Science</source><source>JSTOR Archival Journals and Primary Sources Collection</source><source>Alma/SFX Local Collection</source><creator>Wang, Dashun ; Song, Chaoming ; Shen, Hua-Wei ; Barabási, Albert-László</creator><creatorcontrib>Wang, Dashun ; Song, Chaoming ; Shen, Hua-Wei ; Barabási, Albert-László</creatorcontrib><description>Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach.</description><identifier>ISSN: 0036-8075</identifier><identifier>EISSN: 1095-9203</identifier><identifier>DOI: 10.1126/science.1248961</identifier><identifier>CODEN: SCIEAS</identifier><language>eng</language><publisher>Washington: The American Association for the Advancement of Science</publisher><ispartof>Science (American Association for the Advancement of Science), 2014-07, Vol.345 (6193), p.149-149</ispartof><rights>Copyright © 2014, American Association for the Advancement of Science</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203</citedby><cites>FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,2871,2872,27905,27906</link.rule.ids></links><search><creatorcontrib>Wang, Dashun</creatorcontrib><creatorcontrib>Song, Chaoming</creatorcontrib><creatorcontrib>Shen, Hua-Wei</creatorcontrib><creatorcontrib>Barabási, Albert-László</creatorcontrib><title>Response to Comment on “Quantifying long-term scientific impact”</title><title>Science (American Association for the Advancement of Science)</title><description>Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach.</description><issn>0036-8075</issn><issn>1095-9203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNotkM1KAzEUhYMoWKtrtwHX0-ZnJj9LqVqFgii6Dpl4p0zpJGOSLrrrg-jL9Umc2q4unHs4h_MhdEvJhFImpsm14B1MKCuVFvQMjSjRVaEZ4edoRAgXhSKyukRXKa0IGX6aj9DDO6Q--AQ4BzwLXQc-4-DxfvfztrE-t8229Uu8Dn5ZZIgd_q8Z5Nbhtuuty_vd7zW6aOw6wc3pjtHn0-PH7LlYvM5fZveLwjGhcyEry61kXDDtrLUOqhKg1k0poKY1o1DaL-UsUw6sFEwoWkvOFZMKZH3YMUZ3x9w-hu8NpGxWYRP9UGloVZZcC62rwTU9ulwMKUVoTB_bzsatocQcUJkTKnNCxf8AKitf7Q</recordid><startdate>20140711</startdate><enddate>20140711</enddate><creator>Wang, Dashun</creator><creator>Song, Chaoming</creator><creator>Shen, Hua-Wei</creator><creator>Barabási, Albert-László</creator><general>The American Association for the Advancement of Science</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7SS</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7TM</scope><scope>7U5</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope></search><sort><creationdate>20140711</creationdate><title>Response to Comment on “Quantifying long-term scientific impact”</title><author>Wang, Dashun ; Song, Chaoming ; Shen, Hua-Wei ; Barabási, Albert-László</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Dashun</creatorcontrib><creatorcontrib>Song, Chaoming</creatorcontrib><creatorcontrib>Shen, Hua-Wei</creatorcontrib><creatorcontrib>Barabási, Albert-László</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Science (American Association for the Advancement of Science)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Dashun</au><au>Song, Chaoming</au><au>Shen, Hua-Wei</au><au>Barabási, Albert-László</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Response to Comment on “Quantifying long-term scientific impact”</atitle><jtitle>Science (American Association for the Advancement of Science)</jtitle><date>2014-07-11</date><risdate>2014</risdate><volume>345</volume><issue>6193</issue><spage>149</spage><epage>149</epage><pages>149-149</pages><issn>0036-8075</issn><eissn>1095-9203</eissn><coden>SCIEAS</coden><abstract>Wang, Mei, and Hicks claim that they observed large mean prediction errors when using our model. We find that their claims are a simple consequence of overfitting, which can be avoided by standard regularization methods. Here, we show that our model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach.</abstract><cop>Washington</cop><pub>The American Association for the Advancement of Science</pub><doi>10.1126/science.1248961</doi><tpages>1</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0036-8075 |
ispartof | Science (American Association for the Advancement of Science), 2014-07, Vol.345 (6193), p.149-149 |
issn | 0036-8075 1095-9203 |
language | eng |
recordid | cdi_proquest_journals_1544396995 |
source | American Association for the Advancement of Science; JSTOR Archival Journals and Primary Sources Collection; Alma/SFX Local Collection |
title | Response to Comment on “Quantifying long-term scientific impact” |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T18%3A19%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Response%20to%20Comment%20on%20%E2%80%9CQuantifying%20long-term%20scientific%20impact%E2%80%9D&rft.jtitle=Science%20(American%20Association%20for%20the%20Advancement%20of%20Science)&rft.au=Wang,%20Dashun&rft.date=2014-07-11&rft.volume=345&rft.issue=6193&rft.spage=149&rft.epage=149&rft.pages=149-149&rft.issn=0036-8075&rft.eissn=1095-9203&rft.coden=SCIEAS&rft_id=info:doi/10.1126/science.1248961&rft_dat=%3Cproquest_cross%3E3369066771%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c269t-75a3a723629caaace54eeb9f46eb1b21e4ad8ca28cea762681b7338278e7b9203%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1544396995&rft_id=info:pmid/&rfr_iscdi=true |