„Machine Learning Models“

Suchergebnisse

3.650 Treffer

• Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management
  Costache, Romulus
• Machine learning enables polymer cloud-point engineering via inverse design
  Kumar, Jatin N. ; Li, Qianxiao ; Tang, Karen Y. T. ; Buonassisi, Tonio ; Gonzalez-Oyarce, Anibal L. ; Ye, Jun
• Ensemble models from machine learning: an example of wave runup and coastal dune erosion
  Beuzen, Tomas ; Goldstein, Evan B. ; Splinter, Kristen D.
• Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models
  Chou, Jui-Sheng ; Pham, Thi-Phuong-Trang ; Nguyen, Thi-Kha ; Pham, Anh-Duc ; Ngo, Ngoc-Tri
• Performance Evaluation of Classification Models – Evaluation of Classification models during bank marketing campaign under supervised machine learning domain
  Goel, Sheenam 2019 LAP LAMBERT Academic Publishing
• Building Machine Learning and Deep Learning Models on Google Cloud Platform – A Comprehensive Guide for Beginners
  2019 Apress
• RAISE THE ROOF: TOWARDS GENERATING LOD2 MODELS WITHOUT AERIAL SURVEYS USING MACHINE LEARNING
  Biljecki, F. ; Dehbi, Y.
• Learn PySpark – Build Python-based Machine Learning and Deep Learning Models
  2019 Apress
• Multi-Context Anomaly Detection
  Arslan, Farrukh 2019 LAP LAMBERT Academic Publishing
• Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms
  Zamanirad, Mahtab ; Sarraf, Amirpouya ; Sedghi, Hossein ; Saremi, Ali ; Rezaee, Payman
• Recognition of 3′-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models
  Shein, Alexander ; Zaikin, Anton ; Poptsova, Maria
• Extending Statistical Boosting – An Overview of Recent Methodological Developments
  Mayr, A. ; Binder, H. ; Gefeller, O. ; Schmid, M.
• Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”
  Bühlmann, P. ; Gertheiss, J. ; Hieke, S. ; Kneib, T. ; Ma, S. ; Schumacher, M. ; Tutz, G. ; Wang, C. -Y. ; Wang, Z. ; Ziegler, A.
• The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling
  Mayr, A. ; Binder, H. ; Gefeller, O. ; Schmid, M.
• Prediction of the influenza virus propagation by using different epidemiological and machine learning models
  Schmidt, Carina ; Krayl, Heinrich ; Bounekkar, Ahmed 2019 Hochschule Heilbronn
• Hierarchical Multiclass Topic Modelling with Prior Knowledge
  Schröder, Ken ; Chen, Cathy ; Härdle, Wolfgang 2018 Humboldt-Universität zu Berlin
• Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management
  Hayn, Dieter ; Kreiner, Karl ; Ebner, Hubert ; Kastner, Peter ; Breznik, Nada ; Rzepka, Angelika ; Hofmann, Axel ; Gombotz, Hans ; Schreier, Günter
• Zonal Eddy Viscosity Models Based on Machine Learning
  Matai, R. ; Durbin, P. A.
• Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer
  Abdollahi, Hamid ; Mofid, Bahram ; Shiri, Isaac ; Razzaghdoust, Abolfazl ; Saadipoor, Afshin ; Mahdavi, Arash ; Galandooz, Hassan Maleki ; Mahdavi, Seied Rabi
• Machine learning coarse grained models for water
  Chan, Henry ; Cherukara, Mathew J. ; Narayanan, Badri ; Loeffler, Troy D. ; Benmore, Chris ; Gray, Stephen K. ; Sankaranarayanan, Subramanian K. R. S.