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The original MRF optimization problem is NP-hard and we need to transform it into something easier. is a set of sub-trees of graph where its trees cover all nodes and edges of the main graph. And MRFs defined for every tree in will be smaller.
In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties.
An important innovation of the PAC framework is the introduction of computational complexity ... "Relating Data Compression and Learnability" (PDF). Archived ...
Magnetic resonance fingerprinting (MRF) is methodology in quantitative magnetic resonance imaging (MRI) characterized by a pseudo-randomized acquisition strategy. It involves creating unique signal patterns or 'fingerprints' for different materials or tissues after which a pattern recognition algorithm matches these fingerprints with a predefined dictionary of expected signal patterns.
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
This approach to feature importance for random forests considers as important the variables which decrease a lot the impurity during splitting. [31] It is described in the book Classification and Regression Trees by Leo Breiman [ 32 ] and is the default implementation in sci-kit learn and R .
Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is usually described in its pairing with relevant feature selection as Minimum Redundancy Maximum Relevance (mRMR).
In network theory, link prediction is the problem of predicting the existence of a link between two entities in a network. Examples of link prediction include predicting friendship links among users in a social network, predicting co-authorship links in a citation network, and predicting interactions between genes and proteins in a biological network.