Autonomie decision making based on bayesian networks and ontologies

Aguilar, José; Torres, José F.; Aguilar, Kristell

doi:10.1109/ijcnn.2016.7727693

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Setting epsfnA(i) = ν i as in (7) and epsfnB(i) = µ i as in (8) in Algorithm 1 gives our NONUNIFORM algorithm.…”

Section: Non-uniform Approximate Countersmentioning

confidence: 99%

“…Specializing the NONUNIFORM algorithm for the case of Naïve Bayes, we use results (7) and (8). For each node X i with i ∈ [2, n], K i = J 1 .…”

Section: Naïve Bayesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A Bayesian network can be represented as a directed acyclic graph (DAG), where a node represents a variable and an edge directed from one node to another represents a conditional dependency between the corresponding variables. Bayesian networks have found applications in numerous domains, such as decision making [6,7,8] and cybersecurity [9,10]. While a graphical model can help in reducing the complexity, the number of parameters in such a model can still be quite high, and tracking each parameter independently is expensive, especially in a distributed system that sends a message for each update.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Learning Graphical Models from a Distributed Stream

Tirthapura

Cormode

2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

A current challenge for data management systems is to support the construction and maintenance of machine learning models over data that is large, multi-dimensional, and evolving. While systems that could support these tasks are emerging, the need to scale to distributed, streaming data requires new models and algorithms. In this setting, as well as computational scalability and model accuracy, we also need to minimize the amount of communication between distributed processors, which is the chief component of latency.We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors. We show a strategy for maintaining model parameters that leads to an exponential reduction in communication when compared with baseline approaches to maintain the exact MLE (maximum likelihood estimation). Meanwhile, our strategy provides similar prediction errors for the target distribution and for classification tasks.

show abstract

“…Setting epsfnA(i) = ν i as in (7) and epsfnB(i) = µ i as in (8) in Algorithm 1 gives our NONUNIFORM algorithm.…”

Section: Non-uniform Approximate Countersmentioning

confidence: 99%

“…Specializing the NONUNIFORM algorithm for the case of Naïve Bayes, we use results (7) and (8). For each node X i with i ∈ [2, n], K i = J 1 .…”

Section: Naïve Bayesmentioning

confidence: 99%