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  Browse All Reviews > Mathematics Of Computing (G) > Probability And Statistics (G.3) > Markov Processes (G.3...)  
  1-10 of 96 Reviews about "Markov Processes (G.3...)": Date Reviewed
  MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data
Becker M., Lemmerich F., Singer P., Strohmaier M., Hotho A.  Data Mining and Knowledge Discovery 31(5): 1359-1390, 2017. Type: Article

This well-written paper includes adequate definitions to enable a layperson to understand the principles (generative processes of heterogeneous sequence data of human movement in a city) examined in its simulated experimental study. It uses approp...

Dec 7 2017
  Temporal probabilistic measure for link prediction in collaborative networks
Jaya Lakshmi T., Durga Bhavani S.  Applied Intelligence 47(1): 83-95, 2017. Type: Article

Research on social networks is a fashionable field, along with forecasting the behaviors of entities that are represented as nodes in a graph describing the relationship between entities. The authors investigate opportunities for the improvement o...

Sep 12 2017
  Brain tumor segmentation from multimodal magnetic resonance images via sparse representation
Li Y., Jia F., Qin J.  Artificial Intelligence in Medicine 731-13, 2016. Type: Article

The segmentation of brain tumors in magnetic resonance imaging (MRI) is clearly not only an important image processing task, but one in which the achievement of high accuracy can be life-changing for many. Although MRIs are performed in a number o...

Jul 13 2017
  Markov chain aggregation for agent-based models
Banisch S.,  Springer International Publishing, New York, NY, 2015. 195 pp. Type: Book (978-3-319248-75-2)

Most techniques for modeling dynamic systems fall into one of two categories. Equation-based models such as system dynamics and other differential equation formalisms seek a closed-form expression for the overall dynamics, but typically characteri...

Jul 12 2017
  A general recommendation model for heterogeneous networks
Pham T., Li X., Cong G., Zhang Z.  IEEE Transactions on Knowledge and Data Engineering 28(12): 3140-3153, 2016. Type: Article

Recommenders take data, analyze it, and on the basis of these results offer recommendations to end users about the decisions to make: which restaurant would be the most appropriate considering their preferences and location, what concert to attend...

May 9 2017
  Modeling and performance evaluation of security attacks on opportunistic routing protocols for multihop wireless networks
Salehi M., Boukerche A., Darehshoorzadeh A.  Ad Hoc Networks 5088-101, 2016. Type: Article

The primary aim of opportunistic routing for wireless networks is to improve resilience and increase the probability that data will be delivered from the source node through to the intended destination. The key difference with standard wireless ro...

Nov 16 2016
  Nonlinear Laplacian for digraphs and its applications to network analysis
Yoshida Y.  WSDM 2016 (Proceedings of the 9th ACM International Conference on Web Search and Data Mining, San Francisco, CA,  Feb 22-25, 2016) 483-492, 2016. Type: Proceedings

This paper relates to spectral graph theory and more specifically concerns the case of digraphs, directed graphs. It proposes an alternative framework to existing digraph approaches, such as Chung’s, or the Diplacian, relying on stationary p...

Jul 28 2016
  Exponential moving average based multiagent reinforcement learning algorithms
Awheda M., Schwartz H.  Artificial Intelligence Review 45(3): 299-332, 2016. Type: Article

Reinforcement learning for multiagent systems aims to find optimal policies that can be learned by agents during their interaction in cooperative or competitive games. In game theory, the target is reaching the Nash equilibrium, where each agent i...

Jun 15 2016
  Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases
Khot T., Natarajan S., Kersting K., Shavlik J.  Machine Learning 100(1): 75-100, 2015. Type: Article

This paper describes how a functional-gradient-based boosting algorithm can be used to learn weights of Markov logic networks (MLN). As opposed to earlier methods, which learn the structure of the clauses and the weights in two separate learning s...

Dec 1 2015
  Efficient analysis of probabilistic programs with an unbounded counter
Brázdil T., Kiefer S., Kcera A.  Journal of the ACM 61(6): 1-35, 2014. Type: Article

The authors consider a probabilistic recursive program working on data of unbounded size, for example, with a geometric probability distribution for what the data looks like. This makes sense for common data structures like trees. Is the expected ...

Mar 30 2015
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