Travelled to:1 × Australia
1 × Canada
1 × China
1 × Germany
2 × United Kingdom
3 × USA
Collaborated with:S.Ray J.Davis V.S.Costa ∅ Q.Zeng J.M.Patel H.Blockeel A.Srinivasan J.Liu C.Zhang E.S.Burnside K.Boyd B.Rosell L.Hellerstein C.Phillips S.Adams D.Mehandjiska M.Craven J.W.Shavlik J.Bockhorst J.D.Glasner E.Berg P.L.Peissig M.Caldwell
Talks about:skew (4) multipl (3) learn (3) function (2) predict (2) instanc (2) model (2) tree (2) drug (2) semiparametr (1)
Person: David Page
DBLP: Page:David
Contributed to:
2014
201420122007200520042001
20002000
1998Wrote 13 papers:
- VLDB-2015-ZengPP14 #induction #logic programming #named #scalability
- QuickFOIL: Scalable Inductive Logic Programming (QZ, JMP, DP), pp. 197–208.
- ICML-c2-2014-LiuZBP #dependence #modelling #multi #testing #visual notation
- Multiple Testing under Dependence via Semiparametric Graphical Models (JL, CZ, ESB, DP), pp. 955–963.
- ICML-2012-BoydDPC #empirical #evaluation
- Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation (KB, JD, DP, VSC), p. 210.
- ICML-2012-DavisCBPPC #clustering #predict #relational
- Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events (JD, VSC, EB, DP, PLP, MC), p. 172.
- ICML-2007-DavisCRP #approach #predict #process
- An integrated approach to feature invention and model construction for drug activity prediction (JD, VSC, SR, DP), pp. 217–224.
- ICML-2005-BlockeelPS #learning #multi
- Multi-instance tree learning (HB, DP, AS), pp. 57–64.
- ICML-2005-RayP
- Generalized skewing for functions with continuous and nominal attributes (SR, DP), pp. 705–712.
- ICML-2005-RosellHRP #learning #why
- Why skewing works: learning difficult Boolean functions with greedy tree learners (BR, LH, SR, DP), pp. 728–735.
- ICML-2004-RayP #algorithm
- Sequential skewing: an improved skewing algorithm (SR, DP).
- ICML-2001-RayP #multi
- Multiple Instance Regression (SR, DP), pp. 425–432.
- CL-2000-Page #named
- ILP: Just Do It (DP), pp. 25–40.
- ICML-2000-CravenPSBG #coordination #learning #multi #using
- Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes (MC, DP, JWS, JB, JDG), pp. 199–206.
- TOOLS-PACIFIC-1998-PhillipsAPM #automation #design #object-oriented #user interface
- The Design of the Client User Interface for a Meta Object-Oriented CASE Tool (CP, SA, DP, DM), pp. 156–167.