Response to comment on "Quantifying long-term scientific impact"

Dashun Wang, Chaoming Song, Hua Wei Shen, Albert László Barabási*

*Corresponding author for this work

Research output: Contribution to journalComment/debate

2 Citations (Scopus)

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.

Original languageEnglish (US)
JournalScience
Volume345
Issue number6193
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • General

Cite this

Wang, Dashun ; Song, Chaoming ; Shen, Hua Wei ; Barabási, Albert László. / Response to comment on "Quantifying long-term scientific impact". In: Science. 2014 ; Vol. 345, No. 6193.
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Response to comment on "Quantifying long-term scientific impact". / Wang, Dashun; Song, Chaoming; Shen, Hua Wei; Barabási, Albert László.

In: Science, Vol. 345, No. 6193, 01.01.2014.

Research output: Contribution to journalComment/debate

TY - JOUR

T1 - Response to comment on "Quantifying long-term scientific impact"

AU - Wang, Dashun

AU - Song, Chaoming

AU - Shen, Hua Wei

AU - Barabási, Albert László

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

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U2 - 10.1126/science.1248961

DO - 10.1126/science.1248961

M3 - Comment/debate

C2 - 25013055

AN - SCOPUS:84904100396

VL - 345

JO - Science

JF - Science

SN - 0036-8075

IS - 6193

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