David Heckerman
David Heckerman
Microsoft Research
Verified email at - Homepage
Cited by
Cited by
Causation, prediction, and search
P Spirtes, C Glymour, R Scheines
MIT press, 2001
Learning Bayesian networks: The combination of knowledge and statistical data
D Heckerman, D Geiger, DM Chickering
Machine learning 20, 197-243, 1995
Empirical analysis of predictive algorithms for collaborative filtering
JS Breese, D Heckerman, C Kadie
arXiv preprint arXiv:1301.7363, 2013
A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD
AE Renton, E Majounie, A Waite, J Simón-Sánchez, S Rollinson, ...
Neuron 72 (2), 257-268, 2011
A Bayesian approach to filtering junk e-mail
M Sahami, S Dumais, D Heckerman, E Horvitz
Learning for Text Categorization: Papers from the 1998 workshop 62, 98-105, 1998
Inductive learning algorithms and representations for text categorization
S Dumais, J Platt, D Heckerman, M Sahami
Proceedings of the seventh international conference on Information and …, 1998
Efficient control of population structure in model organism association mapping
HM Kang, NA Zaitlen, CM Wade, A Kirby, D Heckerman, MJ Daly, E Eskin
Genetics 178 (3), 1709-1723, 2008
An MDP-based recommender system.
G Shani, D Heckerman, RI Brafman, C Boutilier
Journal of machine Learning research 6 (9), 2005
FaST linear mixed models for genome-wide association studies
C Lippert, J Listgarten, Y Liu, CM Kadie, RI Davidson, D Heckerman
Nature methods 8 (10), 833-835, 2011
CD8+ T-cell responses to different HIV proteins have discordant associations with viral load
P Kiepiela, K Ngumbela, C Thobakgale, D Ramduth, I Honeyborne, ...
Nature medicine 13 (1), 46-53, 2007
The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users
EJ Horvitz, JS Breese, D Heckerman, D Hovel, K Rommelse
arXiv preprint arXiv:1301.7385, 2013
Bayesian networks for data mining
D Heckerman
Data mining and knowledge discovery 1, 79-119, 1997
Technique which utilizes a probabilistic classifier to detect" junk" e-mail by automatically updating a training and re-training the classifier based on the updated training set
E Horvitz, DE Heckerman, ST Dumais, M Sahami, JC Platt
US Patent 6,161,130, 2000
Large-sample learning of Bayesian networks is NP-hard
M Chickering, D Heckerman, C Meek
Journal of Machine Learning Research 5, 1287-1330, 2004
Intelligent user assistance facility
E Horvitz, JS Breese, DE Heckerman, SD Hobson, DO Hovel, AC Klein, ...
US Patent 6,021,403, 2000
Dependency networks for inference, collaborative filtering, and data visualization
D Heckerman, DM Chickering, C Meek, R Rounthwaite, C Kadie
Journal of Machine Learning Research 1 (Oct), 49-75, 2000
Bayesian factor regression models in the “large p, small n” paradigm
JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, ...
Bayesian statistics 7, 733-742, 2003
Toward normative expert systems: Part i the pathfinder project
DE Heckerman, EJ Horvitz, BN Nathwani
Methods of information in medicine 31 (02), 90-105, 1992
Probabilistic interpretations for MYCIN's certainty factors
D Heckerman
Machine intelligence and pattern recognition 4, 167-196, 1986
Learning gaussian networks
D Geiger, D Heckerman
Uncertainty in Artificial Intelligence, 235-243, 1994
The system can't perform the operation now. Try again later.
Articles 1–20