Results 91 to 100 of about 796,511 (318)
Practical Feature Subset Selection for Machine Learning
, 1998 Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on.
Lloyd A. Smith +3 more
core
FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source
Feature Selection via Coalitional Game Theory
, 2007 We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm
Dror, G., Cohen, S. B., Ruppin, E.
core
A Feature Selection Approach for Emulating the Structure of Mental Representations
, 2011 Tscherepanow M, Kortkamp M, Kühnel S, Helbach J, Schütz C, Schack T. A Feature Selection Approach for Emulating the Structure of Mental Representations. In: Lu B-L, Zhang L, Kwok J, eds.
Marco Kortkamp +14 more
core +1 more source
pietrobarbiero/moea-feature-selection: Absolutno
, 2020 Multi-objective evolutionary algorithms for feature ...
Pietro Barbiero
core +1 more source
FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley +1 more source
Feature subset selection: a correlation based filter approach
, 1997 Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or ...
Hall, Mark A., Smith, Lloyd A.
core
Feature selection for bankruptcy prediction: a multi-objective optimization approach [PDF]
, 2010 In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low.
Vieira, Armando +17 more
core +1 more source
Automatic Feature Selection for Denoising Volumetric Renderings
, 2022 We propose a method for constructing feature sets that significantly improve the quality of neural denoisers for Monte Carlo renderings with volumetric content. Starting from a large set of hand-crafted features, we propose a feature selection process to
Zhang, Xianyao +4 more
core +1 more source
Tau acetylation at K331 has limited impact on tau pathology in vivo
FEBS Letters, EarlyView.We mapped tau post‐translational modifications in humanized MAPT knock‐in mice and in amyloid‐bearing double knock‐in mice. Acetylation within the repeat domain, particularly around K331, showed modest increases under amyloid pathology. To test functional relevance, we generated MAPTK331Q knock‐in mice.
Shoko Hashimoto +3 more
wiley +1 more source