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Open Knowledge
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Travelled to:
1 × Australia
1 × Finland
1 × Germany
1 × Italy
1 × Turkey
1 × United Kingdom
12 × USA
2 × Canada
2 × China
Collaborated with:
D.D.Margineantu P.Tadepalli L.Liu D.Sheldon W.Zhang P.Wu G.Valentini X.Wang V.B.Zubek E.Chown N.S.Flann E.B.Kong H.Almuallim G.Cerbone R.A.Ruff A.Fern A.Ashenfelter Y.Bulatov T.Fountain B.Sudyka M.J.Kearns Y.Mansour H.Hild G.Bakiri T.Sun A.Kumar A.Surve X.Fern N.Mehta S.Ray H.Deng E.N.Mortensen D.Busquets R.L.d.Mántaras C.Sierra C.Jensen H.Lonsdale E.Wynn J.Cao M.Slater N.Larios B.Soran L.G.Shapiro G.Martínez-Muñoz J.Lin S.Natarajan E.Altendorf A.C.Restificar Ethan W. Dereszynski Jesse Hostetler Thao-Trang Hoang Mark Udarbe S.A.Chien B.L.Whitehall R.J.Doyle B.Falkenhainer J.Garrett S.C.Y.Lu T.E.Senator H.G.Goldberg A.Memory W.T.Young B.Rees R.Pierce D.Huang M.Reardon D.A.Bader E.Chow I.A.Essa J.Jones V.Bettadapura D.H.Chau O.Green O.Kaya A.Zakrzewska E.Briscoe R.L.M.IV R.McColl L.Weiss W.Wong S.Das A.Emmott J.Irvine J.Y.Lee D.Koutra C.Faloutsos D.D.Corkill L.Friedland A.Gentzel D.Jensen
Talks about:
learn (16) reinforc (5) model (5) base (4) hierarch (3) improv (3) probabilist (2) knowledg (2) gradient (2) classifi (2)

Person: Thomas G. Dietterich

DBLP DBLP: Dietterich:Thomas_G=

Contributed to:

ICML c2 20142014
ICML c3 20132013
KDD 20132013
CHI 20102010
ICPR 20102010
ICML 20092009
ICML 20082008
ICPR v1 20062006
ICML 20052005
ICML 20042004
ICML 20032003
ICML 20022002
ICML 20002000
KDD 20002000
ICML 19981998
ICML 19971997
ICML 19961996
ICML 19951995
ML 19921992
ML 19911991
ML 19901990
ML 19891989
AIIDE 20112011

Wrote 32 papers:

ICML-c2-2014-LiuD #learning #problem #set
Learnability of the Superset Label Learning Problem (LPL, TGD), pp. 1629–1637.
ICML-c2-2014-LiuSD #approximate #modelling #visual notation
Gaussian Approximation of Collective Graphical Models (LPL, DS, TGD), pp. 1602–1610.
ICML-c3-2013-SheldonSKD #approximate #modelling #visual notation
Approximate Inference in Collective Graphical Models (DS, TS, AK, TGD), pp. 1004–1012.
KDD-2013-SenatorGMYRPHRBCEJBCGKZBMMWDFWDEILKFCFGJ #database #detection #process
Detecting insider threats in a real corporate database of computer usage activity (TES, HGG, AM, WTY, BR, RP, DH, MR, DAB, EC, IAE, JJ, VB, DHC, OG, OK, AZ, EB, RLMI, RM, LW, TGD, AF, WKW, SD, AE, JI, JYL, DK, CF, DDC, LF, AG, DJ), pp. 1393–1401.
CHI-2010-JensenLWCSD #case study
The life and times of files and information: a study of desktop provenance (CJ, HL, EW, JC, MS, TGD), pp. 767–776.
ICPR-2010-LariosSSMLD #identification #kernel #random
Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification (NL, BS, LGS, GMM, JL, TGD), pp. 2624–2627.
ICML-2009-ZhangSFD #learning
Learning non-redundant codebooks for classifying complex objects (WZ, AS, XF, TGD), pp. 1241–1248.
ICML-2008-MehtaRTD #automation
Automatic discovery and transfer of MAXQ hierarchies (NM, SR, PT, TGD), pp. 648–655.
ICPR-v1-2006-ZhangDDM #multi #recognition
A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions (WZ, HD, TGD, ENM), pp. 778–782.
ICML-2005-NatarajanTADFR #first-order #learning #modelling #probability
Learning first-order probabilistic models with combining rules (SN, PT, EA, TGD, AF, ACR), pp. 609–616.
ICML-2004-DietterichAB #random
Training conditional random fields via gradient tree boosting (TGD, AA, YB).
ICML-2004-WuD #data flow
Improving SVM accuracy by training on auxiliary data sources (PW, TGD).
ICML-2003-ValentiniD #bias
Low Bias Bagged Support Vector Machines (GV, TGD), pp. 752–759.
ICML-2003-WangD #learning #modelling #policy
Model-based Policy Gradient Reinforcement Learning (XW, TGD), pp. 776–783.
ICML-2002-DietterichBMS #learning #probability #refinement
Action Refinement in Reinforcement Learning by Probability Smoothing (TGD, DB, RLdM, CS), pp. 107–114.
ICML-2002-ZubekD #heuristic #learning
Pruning Improves Heuristic Search for Cost-Sensitive Learning (VBZ, TGD), pp. 19–26.
ICML-2000-ChownD #approach #divide and conquer #information management #learning
A Divide and Conquer Approach to Learning from Prior Knowledge (EC, TGD), pp. 143–150.
ICML-2000-MargineantuD #classification #evaluation
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers (DDM, TGD), pp. 583–590.
KDD-2000-FountainDS #mining #testing
Mining IC test data to optimize VLSI testing (TF, TGD, BS), pp. 18–25.
ICML-1998-Dietterich #learning
The MAXQ Method for Hierarchical Reinforcement Learning (TGD), pp. 118–126.
ICML-1997-MargineantuD #adaptation
Pruning Adaptive Boosting (DDM, TGD), pp. 211–218.
ICML-1997-TadepalliD #learning
Hierarchical Explanation-Based Reinforcement Learning (PT, TGD), pp. 358–366.
ICML-1996-DietterichKM #framework #learning
Applying the Waek Learning Framework to Understand and Improve C4.5 (TGD, MJK, YM), pp. 96–104.
ICML-1995-DietterichF #learning #perspective
Explanation-Based Learning and Reinforcement Learning: A Unified View (TGD, NSF), pp. 176–184.
ICML-1995-KongD #bias
Error-Correcting Output Coding Corrects Bias and Variance (EBK, TGD), pp. 313–321.
ML-1992-AlmuallimD #concept #learning #on the
On Learning More Concepts (HA, TGD), pp. 11–19.
ML-1991-CerboneD #compilation #optimisation
Knowledge Compilation to Speed Up Numerical Optimization (GC, TGD), pp. 600–604.
ML-1991-ChienWDDFGL #automation #machine learning
Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.
ML-1990-DietterichHB #case study #comparative
A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping (TGD, HH, GB), pp. 24–31.
ML-1989-Dietterich #induction #learning
Limitations on Inductive Learning (TGD), pp. 124–128.
ML-1989-RuffD #question #what
What Good Are Experiments? (RAR, TGD), pp. 109–112.
AIIDE-2011-DereszynskiHFDHU #behaviour #game studies #learning #modelling #probability #realtime
Learning Probabilistic Behavior Models in Real-Time Strategy Games (EWD, JH, AF, TGD, TTH, MU).

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.