18th International Conference on Pattern Recognition (ICPR'06) 2006
DOI: 10.1109/icpr.2006.749
|View full text |Cite
|
Sign up to set email alerts
|

Learning an Optimal Naive Bayes Classifier

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0

Year Published

2009
2009
2022
2022

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 22 publications
(4 citation statements)
references
References 4 publications
0
4
0
Order By: Relevance
“…As part of future work, we consider to use Naïve Bayes with structural improvement [26] to understand the contribution of the different attributes in the predictive relation. Transfer learning strategies may be exploited to migrate population-based models to specific patient-based models.…”
Section: Discussionmentioning
confidence: 99%
“…As part of future work, we consider to use Naïve Bayes with structural improvement [26] to understand the contribution of the different attributes in the predictive relation. Transfer learning strategies may be exploited to migrate population-based models to specific patient-based models.…”
Section: Discussionmentioning
confidence: 99%
“…We validate our LSTM-based model by comparing it to: Logistic Regression Classiier (LRC) [9], Gaussian Naïve Bayes Classiier (NBC) [27], Decision Tree Classiier (DTC) [33], and Recurrent Neural Network (RNN) [21]. We used iFogSim simulator [16] to implement and evaluate our Fog computing architecture.…”
Section: Federated Learning-based Schemementioning
confidence: 99%
“…The probability of predicting the possible class label ci (i = 1,2,…, k) for a particular instance xi is denoted as p(ci| x1,x2,…,xn). In this context, the posterior and prior probability are calculated in order to predict the class label ci for a given instance xi [24,25].…”
Section: ) Nb Classification Algorithmmentioning
confidence: 99%