The following pages link to Scikit (Q20073):
Displaying 50 items.
- A hybrid inference system for improved curvature estimation in the level-set method using machine learning (Q2671404) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- BDD-based optimization for the quadratic stable set problem (Q2673237) (← links)
- Quadratic surface support vector machine with L1 norm regularization (Q2673397) (← links)
- Machine learning the real discriminant locus (Q2674017) (← links)
- Error-correcting neural networks for two-dimensional curvature computation in the level-set method (Q2674272) (← links)
- Modeling the vibrational relaxation rate using machine-learning methods (Q2674733) (← links)
- Research into changes in the level of system noise in high-performance clusters (Q2674993) (← links)
- Stochastic inversion of gravity data accounting for structural uncertainty (Q2675140) (← links)
- Surface warping incorporating machine learning assisted domain likelihood estimation: a new paradigm in mine geology modeling and automation (Q2675143) (← links)
- Machine learning applied to asteroid dynamics (Q2675566) (← links)
- Optimal TSP tour length estimation using standard deviation as a predictor (Q2676394) (← links)
- Optimal TSP tour length estimation using Sammon maps (Q2679001) (← links)
- Reprint of: A forward-backward greedy approach for sparse multiscale learning (Q2679340) (← links)
- Logic explained networks (Q2680793) (← links)
- Quantum locally linear embedding for nonlinear dimensionality reduction (Q2681681) (← links)
- On the stability of citation networks (Q2683113) (← links)
- Optimization of artificial neural networks models applied to the identification of images of asteroids' resonant arguments (Q2685201) (← links)
- Balanced \(k\)-means clustering on an adiabatic quantum computer (Q2685586) (← links)
- Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method (Q2687554) (← links)
- On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573) (← links)
- Data rotation and its influence on quantum encoding (Q2688162) (← links)
- Physical-statistical learning in resilience assessment for power generation systems (Q2690905) (← links)
- Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences (Q2691025) (← links)
- Mixed membership Gaussians (Q2692919) (← links)
- Quantum classifiers for domain adaptation (Q2693860) (← links)
- A direct error correction method for quantum machine learning (Q2693879) (← links)
- Learning dynamical systems using local stability priors (Q2696118) (← links)
- A comparative study of learning techniques for the compressible aerodynamics over a transonic RAE2822 airfoil (Q2698730) (← links)
- pyGPs -- a Python library for Gaussian process regression and classification (Q2788375) (← links)
- Python for Probability, Statistics, and Machine Learning (Q2810201) (← links)
- MLlib: machine learning in Apache Spark (Q2810821) (← links)
- Scaling-up empirical risk minimization: optimization of incomplete \(U\)-statistics (Q2810890) (← links)
- Improved Classification of Known and Unknown Network Traffic Flows Using Semi-supervised Machine Learning (Q2817832) (← links)
- Performance-based numerical solver selection in the Lighthouse framework (Q2830643) (← links)
- Megaman: scalable manifold learning in Python (Q2834476) (← links)
- mlr: machine learning in \(\mathbf R\) (Q2834504) (← links)
- fastFM: a library for factorization machines (Q2834525) (← links)
- Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects (Q2835873) (← links)
- Pystruct-learning structured prediction in Python (Q2934068) (← links)
- (Q2953615) (← links)
- Machine learning algorithms based on generalized Gibbs ensembles (Q3303207) (← links)
- Symbolic Formulae for Linear Mixed Models (Q3305482) (← links)
- Prediction of Neurological Deterioration of Patients with Mild Traumatic Brain Injury Using Machine Learning (Q3305506) (← links)
- Machine Learning for Evolution Strategies (Q4557178) (← links)
- (Q4558140) (← links)
- Random forests, decision trees, and categorical predictors: the ``absent levels'' problem (Q4558194) (← links)
- (Q4558550) (← links)
- Open-Source Libraries, Application Frameworks, and Workflow Systems for NLP (Q4561637) (← links)
- Data-Driven Discovery of Closure Models (Q4562411) (← links)
