Pages that link to "Item:Q2029894"
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The following pages link to Optimization problems for machine learning: a survey (Q2029894):
Displaying 43 items.
- Some approaches to the solution of optimization problems in supervised learning (Q267571) (← links)
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- Machine learning problems from optimization perspective (Q989890) (← links)
- Machine learning for combinatorial optimization: a methodological tour d'horizon (Q2029358) (← links)
- A branch-and-price procedure for clustering data that are graph connected (Q2060392) (← links)
- A maximum-margin multisphere approach for binary multiple instance learning (Q2077935) (← links)
- Special issue optimization for machine learning guest editorial (Q2100397) (← links)
- On constrained smoothing and out-of-range prediction using \(P\)-splines: a conic optimization approach (Q2101966) (← links)
- Operational research and artificial intelligence methods in banking (Q2106712) (← links)
- From inexact optimization to learning via gradient concentration (Q2111477) (← links)
- Machine learning algorithms of relaxation subgradient method with space extension (Q2117655) (← links)
- Learning generalized strong branching for set covering, set packing, and 0-1 knapsack problems (Q2140266) (← links)
- The backbone method for ultra-high dimensional sparse machine learning (Q2163249) (← links)
- Optimization for deep learning: an overview (Q2218095) (← links)
- How can machine learning and optimization help each other better? (Q2218099) (← links)
- On sparse ensemble methods: an application to short-term predictions of the evolution of COVID-19 (Q2239910) (← links)
- A survey of deep network techniques all classifiers can adopt (Q2659274) (← links)
- An exact algorithm for semi-supervised minimum sum-of-squares clustering (Q2676354) (← links)
- A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics (Q2678624) (← links)
- Gradient methods for optimizing metaparameters in the knowledge distillation problem (Q2689575) (← links)
- (Q4011990) (← links)
- From numerical optimization method to learning optimization method (Q5115390) (← links)
- Sublinear optimization for machine learning (Q5395692) (← links)
- Benchmark and Survey of Automated Machine Learning Frameworks (Q5856462) (← links)
- Majorized iPADMM for Nonseparable Convex Minimization Models with Quadratic Coupling Terms (Q6053487) (← links)
- Margin optimal classification trees (Q6065640) (← links)
- An integer programming approach for the hyper-rectangular clustering problem with axis-parallel clusters and outliers (Q6069162) (← links)
- The min-Knapsack problem with compactness constraints and applications in statistics (Q6069243) (← links)
- On mathematical optimization for clustering categories in contingency tables (Q6106171) (← links)
- A regularised fast recursive algorithm for fraction model identification of nonlinear dynamic systems (Q6109495) (← links)
- Navigational guidance -- a deep learning approach (Q6113464) (← links)
- Designing topological data to forecast bankruptcy using convolutional neural networks (Q6115949) (← links)
- Semi-supervised \(k\)-means clustering via DC programming approach (Q6134046) (← links)
- Optimization of sparsity-constrained neural networks as a mixed integer linear program (Q6145048) (← links)
- Hierarchical distributed optimization of constraint-coupled convex and mixed-integer programs using approximations of the dual function (Q6491323) (← links)
- Network flow problem heuristic reduction using machine learning (Q6547704) (← links)
- Lipschitz energy functional for anisotropic diffusion applications (Q6571195) (← links)
- A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data (Q6572858) (← links)
- Explainable real-time predictive analytics on employee workload in digital railway control rooms (Q6572886) (← links)
- Blockchain application for the supply chain optimization (Q6615835) (← links)
- Tuning parameters of deep neural network training algorithms pays off: a computational study (Q6635854) (← links)
- Global optimization: a machine learning approach (Q6667701) (← links)
- An integrated data envelopment analysis and regression tree method for new product price estimation (Q6667803) (← links)
