Ein Verfahren zur Prognose verkehrsabhangiger Schaltzeiten von Lichtsignalanlagen /

Ein Verfahren zur Prognose verkehrsabhangiger Schaltzeiten von Lichtsignalanlagen / / Toni Weisheit.

Saved in:

Bibliographic Details
VerfasserIn:	Weisheit, Toni,
Place / Publishing House:	Kassel, [Germany] : : kassel university press GmbH,, 2017. 2017
Year of Publication:	2017
Language:	German
Series:	Schriftenreihe Verkehr ; Heft 27
Online Access:	https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=4857727
Physical Description:	1 online resource (254 pages).
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep learning : : principios y fundamentos /
by: Bosch Rue, Anna,
Published: (2020.)

Deep Reinforcement Learning zur Steigerung von Energieeffizienz und Pünktlichkeit von Straßenbahnen /
by: Tesar, Markus,
Published: (2023.)

Deep Reinforcement Learning zur Steigerung von Energieeffizienz und Pünktlichkeit von Straßenbahnen /
by: Tesar, Markus,
Published: (2023.)

Beratung als Förderung von Selbstorganisationsprozessen : Empirische Studien zur Beratung von Personen und Organisationen auf der Basis der Synergetik
Published: (2012)

Entwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung /
by: Lohse, Oliver,
Published: (2023.)

Entwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung /
by: Lohse, Oliver,
Published: (2023.)

Gefährliche Forschung? : : Eine Debatte über Gleichheit und Differenz in der Wissenschaft.
by: Hinsch, Wilfried.
Published: (2022.)

Klassifikation von Niederspannungsnetzen mit Support Vector Machines : : Bewertung des Aufnahmevermogens fur Dezentrale Erzeugungsanlagen /
by: Breker, Sebastian,
Published: (2015.)

Machine Learning - Medien, Infrastrukturen und Technologien der Künstlichen Intelligenz /
Published: ([2018])

Modellfreies Lernen optimaler zeitdiskreter Regelungsstrategien für Fertigungsprozesse mit endlichem Zeithorizont /
by: Dornheim, Johannes,
Published: (2022.)

Methoden des Bestärkenden Lernens Für Die Produktionsablaufplanung.
by: Lang, Sebastian.
Published: (2023.)

Digitale Hate Speech : : Interdisziplinäre Perspektiven auf Erkennung, Beschreibung und Regulation /
by: Jaki, Sylvia,
Published: (2023.)

Methoden des bestärkenden Lernens für die Produktionsablaufplanung
by: Lang, Sebastian.
Published: (2023.)

Dynamic Fuzzy Machine Learning /
by: Li, Fanzhang,
Published: ([2017])

Computational thinking : : a beginner's guide to problem-solving and programming /
by: Beecher, Karl ,
Published: (2017.)

Machine learning for OpenCV : : a practical introduction to the world of machine learning and image processing using OpenCV and Python /
by: Beyeler, Michael,
Published: (2017.)

Mastering machine learning with scikit-learn : : learning to implement and evaluate machine learning solutions with scikit-learn /
by: Hackeling, Gavin,
Published: (2017.)

Hands-on deep learning with TensorFlow : : uncover what is underneath your data! /
by: Boxel, Dan Van,
Published: (2017.)

Mastering machine learning with spark 2.x : : create scalable machine learning applications to power a modern data-driven business using spark /
by: Tellez, Alex,
Published: (2017.)

Contributions to machine learning and psychometrics : : computational, graphical, and statistical methods for assessing stability /
by: Philipp, Michel,
Published: (2017.)

Pivoting : : a coach's guide to igniting substantial change /
by: Clancy, Ann L.,
Published: (2017.)

Mastering machine learning with R : : advanced prediction, algorithms, and learning methods with R 3.x /
by: Lesmeister, Cory,
Published: (2017.)

Effective amazon machine learning /
by: Perrier, Alexis ,
Published: (2017.)

Deep learning with Python : : a hands-on introduction /
by: Ketkar, Nikhil,
Published: ([2017])

Advances and Applications in Deep Learning /
Published: (2020.)

Deep learning applications /
Published: ([2021])

Machine Learning : : Algorithms, Models and Applications /
Published: (2021.)

AI and Learning Systems : : Industrial Applications and Future Directions /
Published: (2021.)

Deep Learning : : Research and Applications /
Published: ([2020])

Mathematics of Deep Learning : : An Introduction /
by: Berlyand, Leonid,
Published: ([2023])

Angular and Deep Learning Pocket Primer /
by: Campesato, Oswald,
Published: ([2020])

Regularized System Identification : Learning Dynamic Models from Data
by: Pillonetto, Gianluigi.
Published: (2022.)

Machine Learning /
by: Zhang, Yagang
Published: (2010)

Kernel methods for pattern analysis
by: Shawe-Taylor, John.
Published: (2004.)

Advances in learning theory : methods, models, and applications /
Published: (c2003.)

Mastering TensorFlow 1.x : : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras /
by: Fandango, Armando,
Published: (2018.)

The frontiers of machine learning : : 2017 Raymond and Beverly Sackler U.S -U.K. Scientific Forum /
Published: ([2018])

Deep learning with TensorFlow : : explore neural networks and build intelligent systems with Python /
by: Zaccone, Giancarlo,
Published: (2018.)

Reinforcement learning with TensorFlow : : a beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym /
by: Dutta, Sayon,
Published: (2018.)

Hands-on machine learning on Google cloud platform : : implementing smart and efficient analytics using Cloud ML Engine /
by: Ciaburro, Giuseppe,
Published: (2018.)