2011 - ACM Fellow For contributions to reasoning and decision-making under uncertainty.
2001 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to reasoning and learning under uncertainty.
David Heckerman mostly deals with Artificial intelligence, Machine learning, Bayesian network, Genetics and Immunology. His work carried out in the field of Artificial intelligence brings together such families of science as Speech recognition and Pattern recognition. David Heckerman has included themes like Data mining, Bayesian linear regression, Spambot, Variable-order Bayesian network and Email spam in his Machine learning study.
His Bayesian network research integrates issues from Equivalence, Modularity, Graphical model and Posterior probability, Bayesian probability. His Bayesian probability research is multidisciplinary, incorporating perspectives in Decision tree and Predictive analytics. His Human leukocyte antigen research incorporates elements of Virology, Viral load, Immune system, Epitope and CTL*.
David Heckerman mainly focuses on Artificial intelligence, Machine learning, Bayesian network, Data mining and Virology. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. As part of his studies on Machine learning, David Heckerman often connects relevant areas like Posterior probability.
His Bayesian network research is multidisciplinary, incorporating elements of Variable-order Bayesian network and Theoretical computer science. His research ties Collaborative filtering and Data mining together. David Heckerman combines subjects such as Epitope, Human leukocyte antigen, CTL* and Immunology with his study of Virology.
His scientific interests lie mostly in Artificial intelligence, Bayesian network, Genetics, Virology and Bayesian probability. His Artificial intelligence research includes themes of Machine learning, Contrast and Pattern recognition. His studies examine the connections between Machine learning and genetics, as well as such issues in Debiasing, with regards to Instrumental variable.
David Heckerman has researched Bayesian network in several fields, including Probabilistic logic, Theoretical computer science, Equivalence and Applied mathematics. His work deals with themes such as Epitope, Human leukocyte antigen and CTL*, which intersect with Virology. His Bayesian probability research includes elements of Software and Conditional probability distribution.
His primary areas of study are Genetics, Virology, Epitope, Computational biology and Immune system. His work in Virology addresses issues such as Human leukocyte antigen, which are connected to fields such as CD8 and Mutation. His Computational biology study incorporates themes from Genetic association and Genomics.
His Immune system course of study focuses on HIV vaccine and Conserved sequence, Sequence and In silico. In his study, which falls under the umbrella issue of Data mining, Decision tree and Bayesian network is strongly linked to Collaborative filtering. His Softmax function study introduces a deeper knowledge of Artificial intelligence.
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Empirical analysis of predictive algorithms for collaborative filtering
John S. Breese;David Heckerman;Carl Kadie.
uncertainty in artificial intelligence (1998)
A Tutorial on Learning with Bayesian Networks.
Innovations in Bayesian Networks (2008)
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
David Heckerman;Dan Geiger;David M. Chickering.
Machine Learning (1995)
A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD
Alan E. Renton;Elisa Majounie;Adrian James Waite;Javier Simón-Sánchez;Javier Simón-Sánchez.
Inductive learning algorithms and representations for text categorization
Susan Dumais;John Platt;David Heckerman;Mehran Sahami.
conference on information and knowledge management (1998)
A Bayesian Approach to Filtering Junk E-Mail
Mehran Sahami;Susan Dumais;David Heckerman;Eric Horvitz.
national conference on artificial intelligence (1998)
Efficient Control of Population Structure in Model Organism Association Mapping
Hyun Min Kang;Noah A. Zaitlen;Claire M. Wade;Claire M. Wade;Andrew Kirby;Andrew Kirby.
CD8+ T-cell responses to different HIV proteins have discordant associations with viral load
Photini Kiepiela;Kholiswa Ngumbela;Christina Thobakgale;Dhanwanthie Ramduth.
Nature Medicine (2007)
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
Eric Horvitz;Jack Breese;David Heckerman;David Hovel.
uncertainty in artificial intelligence (1998)
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery (1997)
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