Results 141 to 150 of about 1,169,808 (377)
Learning kμ decision trees on the uniform distribution [PDF]
, 1993 Thomas R. Hancock
openalex +1 more source
, 2020 Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack
Hu, Xiyang +2 more
core
Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth
Advanced Functional Materials, EarlyView.This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh +4 more
wiley +1 more source
Software Defect Prediction Using Extreme Gradient Boosting(XGBoost) with Tune Hyperparameter [PDF]
Al-Rafidain Journal of Computer Sciences and MathematicsSoftware applications have become widely spread in an unprecedented manner in our daily lives, controlling some of the most sensitive and critical aspects within institutions. Examples include automated systems such as traffic control, aviation, and self-
Tariq AL-Hadidi, Safwan Omar Hasoon
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Learning Invariants using Decision Trees
, 2015 15 pages, 2 ...
Krishna, Siddharth +2 more
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Decision support methods in diabetic patient management by insulin administration neural network vs. induction methods for knowledge classification [PDF]
, 2000 Diabetes mellitus is now recognised as a major worldwide public health problem. At present, about 100 million people are registered as diabetic patients. Many clinical, social and economic problems occur as a consequence of insulin-dependent diabetes.
AMBROSIADOU, B. V. +4 more
core
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Possibilistic Induction in Decision-Tree Learning [PDF]
, 2002 We propose a generalization of Ockham's razor, a widely applied principle of inductive inference. This generalization intends to capture the aspect of uncertainty involved in inductive reasoning. To this end, Ockham's razor is formalized within the framework of possibility theory: It is not simply used for identifying a single, apparently optimal model,
openaire +2 more sources
Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore
Advanced Functional Materials, EarlyView.To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul +8 more
wiley +1 more source
IEEE Access, 2018 In this paper, a differential-evolution-based approach implementing a global search strategy to find a near-optimal axis-parallel decision tree is introduced.
Rafael Rivera-Lopez, Juana Canul-Reich
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