Travelled to:
1 × Australia
1 × Canada
1 × China
1 × France
1 × Ireland
1 × Italy
11 × USA
Collaborated with:
∅ D.Sommerfield M.Sahami G.H.John R.Longbotham A.Deng Y.Xu B.Frasca C.Kunz D.Wolpert T.Walker R.M.Henne Z.Zheng L.Mason F.J.Provost T.Fawcett C.Brunk J.Kelly J.Dougherty K.Pfleger T.Crook N.Pohlmann
Talks about:
control (6) experi (6) featur (4) onlin (4) select (3) decis (3) mine (3) web (3) algorithm (2) classifi (2)
Person: Ron Kohavi
DBLP: Kohavi:Ron
Facilitated 1 volumes:
Contributed to:
Wrote 20 papers:
- KDD-2015-Kohavi #lessons learnt #online #testing
- Online Controlled Experiments: Lessons from Running A/B/n Tests for 12 Years (RK), p. 1.
- KDD-2014-KohaviDLX #web
- Seven rules of thumb for web site experimenters (RK, AD, RL, YX), pp. 1857–1866.
- KDD-2013-KohaviDFWXP #online #scalability
- Online controlled experiments at large scale (RK, AD, BF, TW, YX, NP), pp. 1168–1176.
- KDD-2012-KohaviDFLWX #online
- Trustworthy online controlled experiments: five puzzling outcomes explained (RK, AD, BF, RL, TW, YX), pp. 786–794.
- RecSys-2012-Kohavi #online #statistics
- Online controlled experiments: introduction, learnings, and humbling statistics (RK), pp. 1–2.
- KDD-2009-CrookFKL #web
- Seven pitfalls to avoid when running controlled experiments on the web (TC, BF, RK, RL), pp. 1105–1114.
- KDD-2007-KohaviHS #web
- Practical guide to controlled experiments on the web: listen to your customers not to the hippo (RK, RMH, DS), pp. 959–967.
- KDD-2001-Kohavi #e-commerce #mining
- Mining e-commerce data: the good, the bad, and the ugly (RK), pp. 8–13.
- KDD-2001-ZhengKM #algorithm #performance
- Real world performance of association rule algorithms (ZZ, RK, LM), pp. 401–406.
- ICML-1998-ProvostFK #algorithm #estimation #induction
- The Case against Accuracy Estimation for Comparing Induction Algorithms (FJP, TF, RK), pp. 445–453.
- KDD-1998-KohaviS #classification
- Targeting Business Users with Decision Table Classifiers (RK, DS), pp. 249–253.
- ICML-1997-KohaviK
- Option Decision Trees with Majority Votes (RK, CK), pp. 161–169.
- KDD-1997-BrunkKK #data mining #mining #named
- MineSet: An Integrated System for Data Mining (CB, JK, RK), pp. 135–138.
- ICML-1996-KahaviW #bias #composition
- Bias Plus Variance Decomposition for Zero-One Loss Functions (RK, DW), pp. 275–283.
- KDD-1996-Kohavi #classification #hybrid #scalability
- Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid (RK), pp. 202–207.
- KDD-1996-KohaviS
- Error-Based and Entropy-Based Discretization of Continuous Features (RK, MS), pp. 114–119.
- ICML-1995-DoughertyKS
- Supervised and Unsupervised Discretization of Continuous Features (JD, RK, MS), pp. 194–202.
- ICML-1995-KohaviJ #fault #parametricity
- Autmatic Parameter Selection by Minimizing Estimated Error (RK, GHJ), pp. 304–312.
- KDD-1995-KohaviS #set #using
- Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology (RK, DS), pp. 192–197.
- ICML-1994-JohnKP #problem #set
- Irrelevant Features and the Subset Selection Problem (GHJ, RK, KP), pp. 121–129.