Cyberterrorism?

By S. Gordon, Richard Ford. 2002

Subfields: Privacy & Security, Social Sciences & Humanities

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Citations in the corpus

Evasion and Hardening of Tree Ensemble Classifiers (2015, methodology): “For our tree ensembles, BDT is a (gradient) boosted decision trees model in the modern XGBoost implementation (Chen & He) and RF is a random forest trained using scikit-learn (Buitinck et al., 2013).”
Auto-Keras: An Efficient Neural Architecture Search System (2018, methodology): “To use this API, they need to preprocess the dataset by themselves and know which type of neural network, e.g., CNN or MLP, is the best for their task.”
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains (2016, background): “For example, if the agent is a distance of 5 from the goal, the agent receives −5 reward.”
Scikit-learn: Machine Learning Without Learning the Machinery (2015, background): “For the project to be usable in real-world Project vision Scikit-learn was born fairly technical C++ code base.”
PERIODOGRAMS FOR MULTIBAND ASTRONOMICAL TIME SERIES (2015, methodology): “The API for the module is largely influenced by that of the scikit-learn package (Pedregosa et al. 2011; Buitinck et al. 2013)11, in which models are Python class objects which can be fit to data with the fit() method.”
CatBoost for big data: an interdisciplinary review (2020, methodology): “All of these algorithms are available in the Python Scikit-learn library [58] except for XGBoost.”
Bayesian tuning and bandits : an extensible, open source library for AutoML (2018, background): “The API worked like this in order to remain consistent with scikit-learn’s fit and predict API [6].”
How Does Mixup Help With Robustness and Generalization? (2020, methodology): “We use the two-moons dataset (Buitinck et al., 2013).”
A Software Framework for Musical Data Augmentation (2015, methodology): “Finally, we borrow familiar software design patterns from the scikit-learn package [4], such as transformers, pipelines, and model serialization.”
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (2020, methodology): “The GPU-accelerated counterparts to NumPy and SciPy are diverse, giving users many options.”