Python Machine Learning

2015

680 citations recorded by the source corpus.

Citations in the corpus

• Statistical Arbitrage in Cryptocurrency Markets (2019, methodology): “Further details and a comprehensive description of the algorithm are available in Raschka (2015).”
• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “Majority voting system For 3D patch-level, 3D ROI-based and 2D slice-level CNNs, we adopted a soft voting system (Raschka, 2015) to generate the subject-level decision.”
• ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data (2019, background): “Insufficient data causes overfitting and non-generalizability of the model [35].”
• Machine learning–XGBoost analysis of language networks to classify patients with epilepsy (2017, methodology): “SFS and SBSwrapper methodswere performedwithMlxtend Python libraries [40].”
• Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review (2021, methodology): “No optimization is required and its easy to program with high accuracy (Raschka and Mirjalili, 2017).”
• Theory-Guided Machine Learning in Materials Science (2016, unknown): “Scikit-learn developers (2014) and Raschka (2015)].”