Naive Bayes models for probability estimation
By Daniel Lowd, Pedro M. Domingos. International Conference on Machine Learning, 2005
349 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/b2e5a7515d76675961462db97c70f4d2b592acd6
How researchers see this paper
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In Artificial Intelligence
- Yoshua Bengio
- Ruslan Salakhutdinov
- Geoffrey E. Hinton
- Percy Liang
- Quoc V. Le
- Li Deng
- R. Caruana
- Mohit Bansal
In Management Science and Operations Research
- Hoifung Poon
- Zhihua Cai
- Jia Wu
- Alexandru Niculescu-Mizil
- R. Caruana
- Wu He
- Gongjun Yan
- Cristian Bucila
Citations in the corpus
- Developing Vehicular Data Cloud Services in the IoT Environment (2014, methodology): “In this section, we present amodifiedNaïve Bayes model [32] and a Logistic Regression model which have”
- A naive Bayes probability estimation model based on self-adaptive differential evolution (2014, methodology): “However, for highly unbalanced datasets, Laplace-estimates might not be very appropriate (Lowd and Domingos 2005).”
- Machine Learning Algorithms: A Review (2022, neutral): “Based on the probability of belonging to particular class a tree network is created a.k.a. Bayesian network [10].”
- Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, neutral): “…the typical goal of classification algorithms is to maximize accuracy as much as possible, often by building classifiers that are not readable, such as KNNs [Cover and Hart 1967], SVMs [Cortes and Vapnik 1995; Joachims 2006], and Naive Bayes [Domingos and Pazzani 1997; Lowd and Domingos 2005].”
- The Neural Autoregressive Distribution Estimator (2011, background): “Lowd and Domingos (2005) argued that it improves over a Bayesian network with probabilistic decision trees for each conditional.”
- Model compression (2006, methodology): “We experiment with three methods of generating pseudo data: RANDOM, generate data for each attribute independently from its marginal distribution; NBE, estimate the joint density of attributes using the Naive Bayes Estimation algorithm [12] and then generate samples from this joint distribution; and”
- Sum-product networks: A new deep architecture (2011, background): “, mixture models [17], thin junction trees [5]), but are quite limited in the distributions they c an represent compactly.”
- Low Rank Language Models for Small Training Sets (2011, background): “Lowd and Domingos [11] propose Naive Bayes models for estimating arbitrary probability distributions that can be seen as a generalization of (6).”