Suchergebnisse - Computer Learning and Pattern Recognition::Evolutionary Computing*

The reusability prior in deep learning models ; Derin öğrenme modellerinde yeniden kullanılabilirlik önseli

Autoren: Polat, Aydın Göze Alpaslan, Ferda Nur Department of Computer Engineering

Weitere Verfasser: Polat, Aydın Göze Alpaslan, Ferda Nur Department of Computer Engineering

Schlagwörter: Deep learning, Parameter sharing, Genetic algorithms, Reusability, Statistical mechanics, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition::Neural Networks, Computer Learning and Pattern Recognition::Evolutionary Computing, Arbis Subjects::Natural Sciences::Physics::General Physics::Statistical physics, thermodynamic and nonlinear dynamic systems, Arbis Subjects::Engineering and Technology::Computer Sciences::Computer Vision::Computer Vision

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/104170

Verfügbarkeit: https://hdl.handle.net/11511/104170

A STRATEGY BASED ON STATISTICAL MODELLING AND MULTI-OBJECTIVE OPTIMIZATION TO DESIGN A DISHWASHER CLEANING CYCLE ; BULAŞIK MAKİNESi TEMİZLEME DÖNGÜSÜ TASARLAMAK İÇİN İSTATİSTİKSEL MODELLEMEYE VE ÇOK AMAÇLI OPTİMİZASYONA DAYALI BİR STRATEJİ

Autoren: ANAPA, KORKUT Yücel, Hamdullah Bayraktar, Songül et al.

Weitere Verfasser: ANAPA, KORKUT Yücel, Hamdullah Bayraktar, Songül et al.

Schlagwörter: Dishwasher Design, Feature Selection, Multi-Objective Optimization, Evolutionary Algorithms, Statistical Modelling, Arbis Subjects::Natural Sciences::Mathematics::Optimization, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition::Evolutionary Computing, Arbis Subjects::Engineering and Technology::Computer Sciences::Algorithms::Simulation and Modelling, Arbis Subjects::Engineering and Technology::Industrial Engineering::Optimization Theory and Methods::Multiobjective Optimization

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/107595

Verfügbarkeit: https://hdl.handle.net/11511/107595