Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams
Markov chains are simple yet powerful mathematical structures to model temporally
dependent processes. They generally assume stationary data, ie, fixed transition …
dependent processes. They generally assume stationary data, ie, fixed transition …
Learning under concept drift and non-stationary noise: Introduction of the concept of persistence
Learning from noisy data is a challenging task especially when the system under
consideration has a non-stationary nature. The source of the noise is often assumed to be …
consideration has a non-stationary nature. The source of the noise is often assumed to be …
[PDF][PDF] Incremental Learning of Spatio-temporal Markov Chains: an Introductory Theoretical Framework for Composite Domain Markov Chains
Z Kumralbaş, B Tümer - Authorea Preprints, 2024 - techrxiv.org
Temporal dependence (TD) is a fundamental concept exploited for modeling real world
systems' behavior of sequential nature. Markov chains (MCs) are powerful tools capable of …
systems' behavior of sequential nature. Markov chains (MCs) are powerful tools capable of …
Incremental Construction of Markov Chains with Dependence on Non-Temporal Domains
Z Kumralbaş - 2024 - search.proquest.com
Zamansal bağımlılık, birçok gerçek dünya sisteminin ardışıklık içeren davranışını
modellemek için kullanılan temel bir kavramdır. Markov zincirleri, belirtilen olasılık …
modellemek için kullanılan temel bir kavramdır. Markov zincirleri, belirtilen olasılık …