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In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification.
Bacon and Descartes, for their part, also counsel (to varying degrees) a scientific method which builds on experiential knowledge.
This paper presents a method, which builds on a previous implementation, that uses a novel combination of psychophysical modeling and formal verification with model checking to detect masking in a modeled configuration of medical alarms.
Random forest is one type of random subspace learning method which builds an ensemble of classifiers by exploring partial feature spaces (also called subspaces) of the input data [ 69].
Ensembl uses an integrated approach starting from a homology-based method which builds gene-models using species-specific known sequences and proteins from other species aligned to the target genome.
This last is a supervised classification method which builds, based on a training sample, a hierarchy of variables that are used to recursively split the sample into coherent groups, until a certain degree of separation of the sample is achieved.
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Additionally, we explored the different options to define gene-wise P values, including the VEGAS-all method, which built on all the SNPs mapped to a gene, the VEGAS-top method, which used the top 10% SNPs mapped to a gene, and the minP method, which used the most significant SNP.
The other algorithms tested, including several common hierarchical clustering methods which build trees and a method for directly creating a DAG (Local Fitness), struggled with this task by comparison.
For MEPE, the first two methods, which build a protein model by aligning query sequences onto solved template structures, were not feasible.
Here we define and synthesize the "Appropriate-Complexity Method" (ACME), which builds upon these two philosophies to guide the development of process-oriented models across a spectrum of scientific and management objectives.
We compared ANMM4CBR with the mixture of experts for case-based reasoning (MOE4CBR) method [ 12], which builds CBR classifiers based on the idea of mixture of experts.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com